KrusellSmith.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Replicating Maliar, Maliar, and Valli (2010, JEDC) to solve Krusell and Smith (1998, JPE) model using Julia\n",
    "\n",
    "\n",
    "By [Shunsuke Hori](https://github.com/Shunsuke-Hori)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview of the notebook\n",
    "This notebook solves the model of [Krusell and Smith (1998, JPE)](https://www.journals.uchicago.edu/doi/10.1086/250034) and succesfully replicating the result of [Maliar, Maliar, and Valli (2010, JEDC)](https://www.sciencedirect.com/science/article/pii/S0165188909001328).\n",
    "\n",
    "The solution strategy is as follows\n",
    "\n",
    "1. Solve the individual problem by Euler equation method or value function iteration (VFI) with 2D interpolation\n",
    "    - Agents are boundedly rational. In the code, they take into account the information about the mean of capital\n",
    "    - Aggregate law of motion is approximated by log-linear relation, i.e. $\\log(K_{t+1})=B1+B2\\log(K_{t})$ for good aggregate state and $\\log(K_{t+1})=B3+B4\\log(K_{t})$ for bad aggregate state \n",
    "    - If specified, Howard's policy iteration is used\n",
    "1. Compute the path of aggregate capital using the policy function obtained by 1. There are two ways of simulation:\n",
    "    - Monte Carlo following Krusell and Smith (1998). That is, aggregate technology shocks and idiosyncratic employment shocks are drawn for many agents and many periods. Then, using the LLN, the aggregate capital is computed by aggregating all agents for all period.\n",
    "    - Non-stochastic method following [Young (2010, JEDC)](https://www.sciencedirect.com/science/article/pii/S0165188909001316).\n",
    "1. Update the coefficient of aggregate capital law of motion, $B1$, $B2$, $B3$ and $B4$, by regression\n",
    "1. Check convergence of $B1$, $B2$, $B3$ and $B4$\n",
    "\n",
    "NOTE: Regarding interpolation, Krusell and Smith uses various interpolation scheme depending on the purpose, including polynomial interpolation. Maliar, Maliar, and Valli uses spline interpolation in their paper. This notebook only uses linear interpolation.\n",
    "\n",
    "This notebook written in Julia solves the model much faster than the code of Maliar et al. (2010) written in Matlab according to [den Haan (2010, JEDC)](https://www.sciencedirect.com/science/article/pii/S0165188909001298), although they are not simply comparable because initial guess, interpolation methods, and running environments are different."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Code to solve models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First thing to do is importing functions from `KSfunctions.ipynb`.\n",
    "\n",
    "You can find the notebook in the [QuantEcon repositoy](https://github.com/QuantEcon/krusell_smith_code)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Plots.PyPlotBackend()"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using NBInclude\n",
    "@nbinclude(\"KSfunctions.ipynb\")\n",
    "using Plots      # to plot the result\n",
    "pyplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementation by Euler method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, construct a `NamedTuple`, `ksp`, which contains the parameters of the model and `kss` which has initial guess of the solution.\n",
    "\n",
    "(Grid size inconsistency is also checked, which may return error when exiting result is loaded by `load_value=true`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# instance of KSParameter\n",
    "ksp = KSParameter()\n",
    "# instance of KSSolution\n",
    "kss = KSSolution(ksp, load_value=false, load_B=false)\n",
    "if size(kss.k_opt,1) != length(ksp.k_grid)\n",
    "    error(\"loaded data is inconsistent with k_size\")\n",
    "end\n",
    "if size(kss.k_opt,2) != length(ksp.K_grid)\n",
    "    error(\"loaded data is inconsistent with K_size\")\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's draw the shock for stochastic simulation of aggregate law of motion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  2.722285 seconds (3.38 M allocations: 551.803 MiB, 14.96% gc time)\n"
     ]
    }
   ],
   "source": [
    "# generate shocks\n",
    "Random.seed!(0) # for reproducability\n",
    "@time zi_shocks, epsi_shocks =generate_shocks(ksp;\n",
    "        z_shock_size=1100, population=10000);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, the following cell solves the model with Euler equation method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " --- Iteration over ALM coefficient: 1 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:49 (1868 iterations)\u001b[39m)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:01:22\u001b[39m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.45732427121929203 and B = [0.457324, 0.876843, 0.437344, 0.880636]\n",
      " --- Iteration over ALM coefficient: 2 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:58 (2005 iterations)\u001b[39m)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 99%|██████████████████████|  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.013376294857700866 and B = [0.139553, 0.962866, 0.117827, 0.967197]\n",
      " --- Iteration over ALM coefficient: 3 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:52\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:34 (1425 iterations)\u001b[39m)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 99%|██████████████████████|  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.007529530931877415 and B = [0.145434, 0.961389, 0.125839, 0.965189]\n",
      " --- Iteration over ALM coefficient: 4 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:51\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:43 (1536 iterations)\u001b[39m)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 99%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.006751190893555692 and B = [0.146914, 0.96097, 0.127194, 0.964838]\n",
      " --- Iteration over ALM coefficient: 5 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:53\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:40 (1478 iterations)\u001b[39m)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.006949867818305117 and B = [0.149138, 0.960389, 0.130111, 0.964094]\n",
      " --- Iteration over ALM coefficient: 6 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:48\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:37 (1504 iterations)\u001b[39m)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 99%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.006920069330592693 and B = [0.151193, 0.959844, 0.133629, 0.963172]\n",
      " --- Iteration over ALM coefficient: 7 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:50\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:35 (1493 iterations)\u001b[39m)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.00694718079446266 and B = [0.152435, 0.959511, 0.136544, 0.962399]\n",
      " --- Iteration over ALM coefficient: 8 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:47\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:37 (1457 iterations)\u001b[39m)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 99%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.006722163986636137 and B = [0.153018, 0.959354, 0.138403, 0.961906]\n",
      " --- Iteration over ALM coefficient: 9 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:52\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:42 (1417 iterations)\u001b[39m)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 99%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.005897742408243367 and B = [0.153293, 0.959281, 0.139595, 0.961591]\n",
      " --- Iteration over ALM coefficient: 10 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:50\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:34 (1377 iterations)\u001b[39m)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.005054266904291199 and B = [0.15346, 0.959236, 0.140521, 0.961346]\n",
      " --- Iteration over ALM coefficient: 11 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:50\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:33 (1335 iterations)\u001b[39m)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 99%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.004035289699137801 and B = [0.15353, 0.959218, 0.141018, 0.961214]\n",
      " --- Iteration over ALM coefficient: 12 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:52\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:40 (1290 iterations)\u001b[39m)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 99%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.0032224775860056343 and B = [0.153588, 0.959202, 0.141416, 0.961109]\n",
      " --- Iteration over ALM coefficient: 13 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:56\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:30 (1252 iterations)\u001b[39m)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.002538977211810678 and B = [0.153601, 0.959199, 0.141699, 0.961034]\n",
      " --- Iteration over ALM coefficient: 14 ---"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:47\u001b[39m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:29 (1203 iterations)\u001b[39m)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.001943945722092888 and B = [0.153602, 0.959199, 0.141866, 0.96099]\n",
      " --- Iteration over ALM coefficient: 15 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:50\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:32 (1152 iterations)\u001b[39m)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.0015086526630891939 and B = [0.153583, 0.959203, 0.142014, 0.96095]\n",
      " --- Iteration over ALM coefficient: 16 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:01:00\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:36 (1087 iterations)\u001b[39m)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 99%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.0010862755331306762 and B = [0.153549, 0.959212, 0.142044, 0.960942]\n",
      " --- Iteration over ALM coefficient: 17 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:51\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:29 (1020 iterations)\u001b[39m)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.0008088499380103642 and B = [0.153519, 0.95922, 0.142093, 0.960929]\n",
      " --- Iteration over ALM coefficient: 18 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:53\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:25 (932 iterations)\u001b[39m)\u001b[39mm\n",
      "\u001b[32msimulating aggregate path ... 99%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.0005739777491678977 and B = [0.153486, 0.959228, 0.1421, 0.960926]\n",
      " --- Iteration over ALM coefficient: 19 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:54\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:23 (815 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 99%|██████████████████████|  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.00039404617945887344 and B = [0.153448, 0.959238, 0.142093, 0.960928]\n",
      " --- Iteration over ALM coefficient: 20 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:57\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:19 (758 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 99%|██████████████████████|  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.00023283842657845222 and B = [0.153414, 0.959247, 0.14205, 0.960939]\n",
      " --- Iteration over ALM coefficient: 21 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:52\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:18 (740 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.00013822097346735673 and B = [0.153397, 0.959251, 0.142025, 0.960946]\n",
      " --- Iteration over ALM coefficient: 22 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:01:04\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:20 (673 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 8.06687554061325e-5 and B = [0.153392, 0.959253, 0.142009, 0.96095]\n",
      " --- Iteration over ALM coefficient: 23 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:01:27\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:25 (503 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 5.315201402597558e-5 and B = [0.15339, 0.959253, 0.142005, 0.960951]\n",
      " --- Iteration over ALM coefficient: 24 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:60\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:11 (405 iterations)\u001b[39m)\u001b[39mm\n",
      "\u001b[32msimulating aggregate path ... 99%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 3.499610298809963e-5 and B = [0.153389, 0.959254, 0.142003, 0.960952]\n",
      " --- Iteration over ALM coefficient: 25 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:59\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:10 (331 iterations)\u001b[39m)\u001b[39mm\n",
      "\u001b[32msimulating aggregate path ... 99%|██████████████████████|  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 2.3276446504849613e-5 and B = [0.153389, 0.959254, 0.142002, 0.960952]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████|  ETA: 0:00:00\u001b[39m\r",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:01:05\u001b[39m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- Iteration over ALM coefficient: 26 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:09 (267 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.5554764154823708e-5 and B = [0.153389, 0.959254, 0.142001, 0.960952]\n",
      " --- Iteration over ALM coefficient: 27 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:01:08\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:06 (215 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 99%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.0427608073432237e-5 and B = [0.153389, 0.959254, 0.142001, 0.960952]\n",
      " --- Iteration over ALM coefficient: 28 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:55\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:04 (170 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 99%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 7.004536259735961e-6 and B = [0.153389, 0.959254, 0.142001, 0.960952]\n",
      " --- Iteration over ALM coefficient: 29 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:59\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:04 (133 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 99%|██████████████████████|  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 4.7113688405875376e-6 and B = [0.153389, 0.959254, 0.142, 0.960952]\n",
      " --- Iteration over ALM coefficient: 30 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:01:01\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:03 (101 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 99%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 3.1725284229677797e-6 and B = [0.153389, 0.959254, 0.142, 0.960953]\n",
      " --- Iteration over ALM coefficient: 31 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:01:04\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:02 (76 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 2.1384109868127776e-6 and B = [0.153389, 0.959254, 0.142, 0.960953]\n",
      " --- Iteration over ALM coefficient: 32 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:59\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:02 (59 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 99%|██████████████████████|  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.4428119854970056e-6 and B = [0.153389, 0.959254, 0.142, 0.960953]\n",
      " --- Iteration over ALM coefficient: 33 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:53\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:01 (49 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 99%|██████████████████████|  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 9.746580759972812e-7 and B = [0.153389, 0.959254, 0.142, 0.960953]\n",
      " --- Iteration over ALM coefficient: 34 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:01:12\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:01 (40 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 6.604192821224597e-7 and B = [0.153389, 0.959254, 0.142, 0.960953]\n",
      " --- Iteration over ALM coefficient: 35 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:01:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:01 (32 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 99%|██████████████████████|  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 4.4834295784279554e-7 and B = [0.153389, 0.959254, 0.142, 0.960953]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████|  ETA: 0:00:00\u001b[39m\r",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:52\u001b[39m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " --- Iteration over ALM coefficient: 36 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:01 (26 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 99%|██████████████████████|  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 3.044883026270817e-7 and B = [0.153389, 0.959254, 0.142, 0.960953]\n",
      " --- Iteration over ALM coefficient: 37 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:01:07\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:01 (21 iterations)\u001b[39m)\u001b[39m9m\n",
      "\u001b[32msimulating aggregate path ... 99%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 2.0721216803520903e-7 and B = [0.153389, 0.959254, 0.142, 0.960953]\n",
      " --- Iteration over ALM coefficient: 38 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:54\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:01 (18 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 99%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.4109155307973076e-7 and B = [0.153389, 0.959254, 0.142, 0.960953]\n",
      " --- Iteration over ALM coefficient: 39 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:59\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:00 (16 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 99%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 9.618399712807424e-8 and B = [0.153389, 0.959254, 0.142, 0.960953]\n",
      " --- Iteration over ALM coefficient: 40 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:59\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:00 (13 iterations)\u001b[39m8)\u001b[39mm\n",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 6.608575678357376e-8 and B = [0.153389, 0.959254, 0.142, 0.960953]\n",
      " --- Iteration over ALM coefficient: 41 ---"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:55\u001b[39m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:00 (11 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 4.555757640578584e-8 and B = [0.153389, 0.959254, 0.142, 0.960953]\n",
      " --- Iteration over ALM coefficient: 42 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:01:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:00 (9 iterations)\u001b[39m08)\u001b[39mm\n",
      "\u001b[32msimulating aggregate path ... 99%|██████████████████████|  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 3.1577832876328316e-8 and B = [0.153389, 0.959254, 0.142, 0.960953]\n",
      " --- Iteration over ALM coefficient: 43 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:01:11\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:00 (8 iterations)\u001b[39m-08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 99%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 2.178663283114446e-8 and B = [0.153389, 0.959254, 0.142, 0.960953]\n",
      " --- Iteration over ALM coefficient: 44 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:52\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:00 (6 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 99%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.5266983621753383e-8 and B = [0.153389, 0.959254, 0.142, 0.960953]\n",
      " --- Iteration over ALM coefficient: 45 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:56\u001b[39m\n",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.0838198061247084e-8 and B = [0.153389, 0.959254, 0.142, 0.960953]\n",
      " --- Iteration over ALM coefficient: 46 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:01:02\u001b[39m\n",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 7.593742634703915e-9 and B = [0.153389, 0.959254, 0.142, 0.960953]\n",
      "-----------------------------------------------------\n",
      "ALM coefficient successfully converged : dif = 7.593742634703915e-9\n",
      "-----------------------------------------------------\n",
      "3494.574072 seconds (19.39 G allocations: 2.464 TiB, 11.55% gc time)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:01:03\u001b[39m\n"
     ]
    }
   ],
   "source": [
    "# find ALM coefficient\n",
    "sm = Stochastic(epsi_shocks)\n",
    "T_discard = 100\n",
    "@time K_ts = find_ALM_coef!(EulerMethod(),\n",
    "            sm, ksp, kss, zi_shocks, \n",
    "            tol_ump = 1e-8, max_iter_ump = 10000,\n",
    "            tol_B = 1e-8, max_iter_B = 500, update_B = 0.3,\n",
    "            T_discard = T_discard);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare the true aggreate law of motion for capital and approximated one with figure and regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_ALM(ksp.z_grid, zi_shocks, kss.B,K_ts, T_discard = T_discard)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Approximated aggregate capital law of motion\n",
      "log(K_{t+1})=0.1533888678582281+0.9592537018105605log(K_{t}) in good time (R2 = 0.9999965851470959)\n",
      "log(K_{t+1})=0.14199999106533273+0.9609525795431593log(K_{t}) in bad time (R2 = 0.9999940336791976)\n"
     ]
    }
   ],
   "source": [
    "#kss.B  # Regression coefficient\n",
    "println(\"Approximated aggregate capital law of motion\")\n",
    "println(\"log(K_{t+1})=$(kss.B[1])+$(kss.B[2])log(K_{t}) in good time (R2 = $(kss.R2[1]))\")\n",
    "println(\"log(K_{t+1})=$(kss.B[3])+$(kss.B[4])log(K_{t}) in bad time (R2 = $(kss.R2[2]))\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The approximated law of motion of capital is very close to the true one, which implies that assuming agents are partially rational is not bad idea since the difference of their actions are negligible.\n",
    "\n",
    "The mean of capital, about 40, is  sufficiently close to Maliar et al. (2010)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "@save \"result_Euler.jld2\" ksp kss "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean of capital implied by regression is 40.46934609899746\n",
      "mean of capital implied by simulation is 40.67905048880162\n"
     ]
    }
   ],
   "source": [
    "# Compute mean of capital implied by regression\n",
    "mc=MarkovChain(ksp.transmat.Pz)\n",
    "sd=stationary_distributions(mc)[1]\n",
    "logKg=kss.B[1]/(1-kss.B[2])\n",
    "logKb=kss.B[3]/(1-kss.B[4])\n",
    "meanK_reg=exp(sd[1]*logKg+sd[2]*logKb)\n",
    "meanK_sim=mean(K_ts[T_discard+1:end])\n",
    "println(\"mean of capital implied by regression is $meanK_reg\")\n",
    "println(\"mean of capital implied by simulation is $meanK_sim\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figures in Krusell-Smith"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, plot the replication figure:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_Fig1(ksp ,kss, K_ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_Fig2(ksp, kss, 40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both figures are replicated well. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solution with Young (2008)'s method\n",
    "In this section, aggregate capital is simulated by the method of Young (2008)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " --- Iteration over ALM coefficient: 1 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:52 (1868 iterations)\u001b[39m)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 73%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.4561348316213136 and B = [0.456135, 0.877212, 0.436797, 0.880838]\n",
      " --- Iteration over ALM coefficient: 2 ---"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[32msimulating aggregate path ... 98%|██████████████████████|  ETA: 0:00:00\u001b[39m\r",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:03\u001b[39m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:48 (2006 iterations)\u001b[39m)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 92%|█████████████████████ |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.008617043773197455 and B = [0.142877, 0.962002, 0.122422, 0.966001]\n",
      " --- Iteration over ALM coefficient: 3 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:31 (1375 iterations)\u001b[39m)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 74%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.007375020667367477 and B = [0.146027, 0.961212, 0.128755, 0.964392]\n",
      " --- Iteration over ALM coefficient: 4 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:34 (1464 iterations)\u001b[39m)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 73%|████████████████      |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.005267484488800378 and B = [0.146131, 0.961154, 0.129092, 0.9643]\n",
      " --- Iteration over ALM coefficient: 5 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:32 (1345 iterations)\u001b[39m)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 75%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.0040070314216140435 and B = [0.146451, 0.961065, 0.130024, 0.964064]\n",
      " --- Iteration over ALM coefficient: 6 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:31 (1329 iterations)\u001b[39m)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 74%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.002959894299560145 and B = [0.146606, 0.961018, 0.130712, 0.963886]\n",
      " --- Iteration over ALM coefficient: 7 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:29 (1278 iterations)\u001b[39m)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 73%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.002149672093765459 and B = [0.146684, 0.960994, 0.131256, 0.963744]\n",
      " --- Iteration over ALM coefficient: 8 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:28 (1228 iterations)\u001b[39m)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 83%|███████████████████   |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.001557972215212089 and B = [0.146711, 0.960985, 0.131645, 0.963643]\n",
      " --- Iteration over ALM coefficient: 9 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:03\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:27 (1176 iterations)\u001b[39m)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 77%|█████████████████     |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.0013627235518748892 and B = [0.146715, 0.960982, 0.131917, 0.963572]\n",
      " --- Iteration over ALM coefficient: 10 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:26 (1124 iterations)\u001b[39m)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.0011429971537960382 and B = [0.146711, 0.960983, 0.132106, 0.963522]\n",
      " --- Iteration over ALM coefficient: 11 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:26 (1075 iterations)\u001b[39m)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 75%|█████████████████     |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.000933283892693676 and B = [0.146706, 0.960983, 0.132239, 0.963487]\n",
      " --- Iteration over ALM coefficient: 12 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:24 (1028 iterations)\u001b[39m)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 75%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.0007498547904362107 and B = [0.146704, 0.960984, 0.132336, 0.963462]\n",
      " --- Iteration over ALM coefficient: 13 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:23 (985 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 74%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.0005976751652848611 and B = [0.146705, 0.960983, 0.132409, 0.963443]\n",
      " --- Iteration over ALM coefficient: 14 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:22 (947 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 75%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.0004756173132779984 and B = [0.146711, 0.960982, 0.132466, 0.963428]\n",
      " --- Iteration over ALM coefficient: 15 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:21 (912 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 76%|█████████████████     |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.00037978251670364727 and B = [0.146718, 0.96098, 0.132513, 0.963416]\n",
      " --- Iteration over ALM coefficient: 16 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:21 (880 iterations)\u001b[39m)\u001b[39mm\n",
      "\u001b[32msimulating aggregate path ... 74%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.0003054532672387422 and B = [0.146728, 0.960977, 0.132552, 0.963406]\n",
      " --- Iteration over ALM coefficient: 17 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:19 (852 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 73%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.00024810888217277616 and B = [0.146738, 0.960974, 0.132587, 0.963397]\n",
      " --- Iteration over ALM coefficient: 18 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:19 (827 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 75%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.000203855571243583 and B = [0.146748, 0.960972, 0.132617, 0.963389]\n",
      " --- Iteration over ALM coefficient: 19 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:18 (804 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 73%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.00016953933124272402 and B = [0.146759, 0.960969, 0.132644, 0.963382]\n",
      " --- Iteration over ALM coefficient: 20 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:18 (782 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 76%|█████████████████     |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.00014270299955676524 and B = [0.146768, 0.960966, 0.132668, 0.963375]\n",
      " --- Iteration over ALM coefficient: 21 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:17 (762 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 73%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.00012147997353278983 and B = [0.146778, 0.960964, 0.132689, 0.96337]\n",
      " --- Iteration over ALM coefficient: 22 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:17 (744 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 93%|█████████████████████ |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.00010447742525301895 and B = [0.146786, 0.960962, 0.132709, 0.963365]\n",
      " --- Iteration over ALM coefficient: 23 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:17 (725 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 75%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 9.066849963249024e-5 and B = [0.146794, 0.96096, 0.132726, 0.96336]\n",
      " --- Iteration over ALM coefficient: 24 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:16 (708 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 73%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 7.929176853394004e-5 and B = [0.146802, 0.960958, 0.132742, 0.963356]\n",
      " --- Iteration over ALM coefficient: 25 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:16 (691 iterations)\u001b[39m)\u001b[39mm\n",
      "\u001b[32msimulating aggregate path ... 74%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 6.97903851978876e-5 and B = [0.146809, 0.960956, 0.132756, 0.963352]\n",
      " --- Iteration over ALM coefficient: 26 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:15 (674 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 76%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 6.175582296052973e-5 and B = [0.146815, 0.960954, 0.132769, 0.963349]\n",
      " --- Iteration over ALM coefficient: 27 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:15 (658 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 73%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 5.488468390446588e-5 and B = [0.14682, 0.960953, 0.132781, 0.963346]\n",
      " --- Iteration over ALM coefficient: 28 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:14 (642 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 95%|█████████████████████ |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 4.89512334236053e-5 and B = [0.146825, 0.960952, 0.132792, 0.963343]\n",
      " --- Iteration over ALM coefficient: 29 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:14 (626 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 76%|█████████████████     |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 4.37845498754974e-5 and B = [0.14683, 0.960951, 0.132801, 0.96334]\n",
      " --- Iteration over ALM coefficient: 30 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:14 (610 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 74%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 3.92541615132358e-5 and B = [0.146834, 0.960949, 0.13281, 0.963338]\n",
      " --- Iteration over ALM coefficient: 31 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:13 (595 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 72%|████████████████      |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 3.5258409811500524e-5 and B = [0.146838, 0.960948, 0.132817, 0.963336]\n",
      " --- Iteration over ALM coefficient: 32 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:13 (579 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 3.1717546197990165e-5 and B = [0.146841, 0.960948, 0.132824, 0.963334]\n",
      " --- Iteration over ALM coefficient: 33 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:13 (564 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 73%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 2.8567378619087647e-5 and B = [0.146844, 0.960947, 0.132831, 0.963333]\n",
      " --- Iteration over ALM coefficient: 34 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:13 (548 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 73%|████████████████      |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 2.5756294767614873e-5 and B = [0.146847, 0.960946, 0.132837, 0.963331]\n",
      " --- Iteration over ALM coefficient: 35 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:12 (533 iterations)\u001b[39m)\u001b[39mm\n",
      "\u001b[32msimulating aggregate path ... 74%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 2.3240819451986194e-5 and B = [0.14685, 0.960945, 0.132842, 0.96333]\n",
      " --- Iteration over ALM coefficient: 36 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:12 (518 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 72%|████████████████      |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 2.0985733613115842e-5 and B = [0.146852, 0.960945, 0.132847, 0.963329]\n",
      " --- Iteration over ALM coefficient: 37 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:12 (503 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 76%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.896030110459801e-5 and B = [0.146854, 0.960944, 0.132851, 0.963327]\n",
      " --- Iteration over ALM coefficient: 38 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:11 (488 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 75%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.713855817897514e-5 and B = [0.146856, 0.960944, 0.132855, 0.963326]\n",
      " --- Iteration over ALM coefficient: 39 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:11 (473 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 75%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.5498205413494448e-5 and B = [0.146858, 0.960943, 0.132858, 0.963325]\n",
      " --- Iteration over ALM coefficient: 40 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:10 (458 iterations)\u001b[39m)\u001b[39mm\n",
      "\u001b[32msimulating aggregate path ... 75%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.4019786232499776e-5 and B = [0.146859, 0.960943, 0.132861, 0.963325]\n",
      " --- Iteration over ALM coefficient: 41 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:10 (443 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 75%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.2686044406962083e-5 and B = [0.146861, 0.960943, 0.132864, 0.963324]\n",
      " --- Iteration over ALM coefficient: 42 ---"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:10 (429 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 73%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.1481971272403468e-5 and B = [0.146862, 0.960942, 0.132867, 0.963323]\n",
      " --- Iteration over ALM coefficient: 43 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:09 (414 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 74%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.0394878791397666e-5 and B = [0.146863, 0.960942, 0.132869, 0.963323]\n",
      " --- Iteration over ALM coefficient: 44 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:09 (400 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 73%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 9.412208783921017e-6 and B = [0.146864, 0.960942, 0.132871, 0.963322]\n",
      " --- Iteration over ALM coefficient: 45 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:09 (385 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 72%|████████████████      |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 8.523977039931463e-6 and B = [0.146865, 0.960941, 0.132873, 0.963322]\n",
      " --- Iteration over ALM coefficient: 46 ---"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[32msimulating aggregate path ... 96%|██████████████████████|  ETA: 0:00:00\u001b[39m\r",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:09 (371 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 73%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 7.72064266219008e-6 and B = [0.146866, 0.960941, 0.132875, 0.963321]\n",
      " --- Iteration over ALM coefficient: 47 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:08 (357 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 76%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 6.994028114354345e-6 and B = [0.146867, 0.960941, 0.132877, 0.963321]\n",
      " --- Iteration over ALM coefficient: 48 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:08 (344 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 75%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 6.336323091604701e-6 and B = [0.146867, 0.960941, 0.132878, 0.96332]\n",
      " --- Iteration over ALM coefficient: 49 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:08 (330 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 72%|████████████████      |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 5.741289606842059e-6 and B = [0.146868, 0.960941, 0.132879, 0.96332]\n",
      " --- Iteration over ALM coefficient: 50 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:07 (316 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 5.20255477609588e-6 and B = [0.146869, 0.96094, 0.132881, 0.96332]\n",
      " --- Iteration over ALM coefficient: 51 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:07 (303 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 75%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 4.714489223428542e-6 and B = [0.146869, 0.96094, 0.132882, 0.963319]\n",
      " --- Iteration over ALM coefficient: 52 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:07 (290 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 74%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 4.272670644511756e-6 and B = [0.14687, 0.96094, 0.132883, 0.963319]\n",
      " --- Iteration over ALM coefficient: 53 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:06 (277 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 73%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 3.8725663447647385e-6 and B = [0.14687, 0.96094, 0.132883, 0.963319]\n",
      " --- Iteration over ALM coefficient: 54 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:06 (264 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 72%|████████████████      |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 3.5099821105444295e-6 and B = [0.14687, 0.96094, 0.132884, 0.963319]\n",
      " --- Iteration over ALM coefficient: 55 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:06 (252 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 75%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 3.1813702160432555e-6 and B = [0.146871, 0.96094, 0.132885, 0.963318]\n",
      " --- Iteration over ALM coefficient: 56 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:05 (239 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 94%|█████████████████████ |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 2.884014287607428e-6 and B = [0.146871, 0.96094, 0.132886, 0.963318]\n",
      " --- Iteration over ALM coefficient: 57 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:05 (228 iterations)\u001b[39m)\u001b[39mm\n",
      "\u001b[32msimulating aggregate path ... 75%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 2.613897075925964e-6 and B = [0.146871, 0.96094, 0.132886, 0.963318]\n",
      " --- Iteration over ALM coefficient: 58 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:05 (216 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 74%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 2.3698285921691653e-6 and B = [0.146872, 0.96094, 0.132887, 0.963318]\n",
      " --- Iteration over ALM coefficient: 59 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:05 (204 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 74%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 2.1483925622678424e-6 and B = [0.146872, 0.96094, 0.132887, 0.963318]\n",
      " --- Iteration over ALM coefficient: 60 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:04 (193 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 75%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.947404442897982e-6 and B = [0.146872, 0.96094, 0.132888, 0.963318]\n",
      " --- Iteration over ALM coefficient: 61 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:04 (182 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 96%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.7653895722224977e-6 and B = [0.146872, 0.96094, 0.132888, 0.963318]\n",
      " --- Iteration over ALM coefficient: 62 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:04 (172 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 76%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.6003569559552844e-6 and B = [0.146873, 0.960939, 0.132888, 0.963318]\n",
      " --- Iteration over ALM coefficient: 63 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:04 (161 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 93%|█████████████████████ |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.4512411514178147e-6 and B = [0.146873, 0.960939, 0.132889, 0.963317]\n",
      " --- Iteration over ALM coefficient: 64 ---"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:03 (151 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 74%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.3154069426757342e-6 and B = [0.146873, 0.960939, 0.132889, 0.963317]\n",
      " --- Iteration over ALM coefficient: 65 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:03 (142 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 73%|████████████████      |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.1924123506645845e-6 and B = [0.146873, 0.960939, 0.132889, 0.963317]\n",
      " --- Iteration over ALM coefficient: 66 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:03 (132 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 74%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.0814044617279972e-6 and B = [0.146873, 0.960939, 0.13289, 0.963317]\n",
      " --- Iteration over ALM coefficient: 67 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:03 (123 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 73%|████████████████      |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 9.802967592675849e-7 and B = [0.146873, 0.960939, 0.13289, 0.963317]\n",
      " --- Iteration over ALM coefficient: 68 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:03 (115 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 74%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 8.885985442186328e-7 and B = [0.146873, 0.960939, 0.13289, 0.963317]\n",
      " --- Iteration over ALM coefficient: 69 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:02 (106 iterations)\u001b[39m8)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 73%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 8.059627235879052e-7 and B = [0.146873, 0.960939, 0.13289, 0.963317]\n",
      " --- Iteration over ALM coefficient: 70 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:02 (98 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 74%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 7.306925396410868e-7 and B = [0.146873, 0.960939, 0.13289, 0.963317]\n",
      " --- Iteration over ALM coefficient: 71 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:02 (91 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 73%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 6.623617018197692e-7 and B = [0.146874, 0.960939, 0.132891, 0.963317]\n",
      " --- Iteration over ALM coefficient: 72 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:02 (83 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 72%|████████████████      |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 6.010190553018546e-7 and B = [0.146874, 0.960939, 0.132891, 0.963317]\n",
      " --- Iteration over ALM coefficient: 73 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:02 (77 iterations)\u001b[39m8)\u001b[39mm\n",
      "\u001b[32msimulating aggregate path ... 74%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 5.445665013925627e-7 and B = [0.146874, 0.960939, 0.132891, 0.963317]\n",
      " --- Iteration over ALM coefficient: 74 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:02 (70 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 73%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 4.942435950783253e-7 and B = [0.146874, 0.960939, 0.132891, 0.963317]\n",
      " --- Iteration over ALM coefficient: 75 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:02 (65 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 79%|██████████████████    |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 4.477099435074017e-7 and B = [0.146874, 0.960939, 0.132891, 0.963317]\n",
      " --- Iteration over ALM coefficient: 76 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:03\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:01 (59 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 75%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 4.0644643303777706e-7 and B = [0.146874, 0.960939, 0.132891, 0.963317]\n",
      " --- Iteration over ALM coefficient: 77 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:01 (55 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 73%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 3.6810898323969e-7 and B = [0.146874, 0.960939, 0.132891, 0.963317]\n",
      " --- Iteration over ALM coefficient: 78 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:01 (50 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 74%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 3.342283573515292e-7 and B = [0.146874, 0.960939, 0.132891, 0.963317]\n",
      " --- Iteration over ALM coefficient: 79 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:01 (47 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 74%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 3.024636119897117e-7 and B = [0.146874, 0.960939, 0.132891, 0.963317]\n",
      " --- Iteration over ALM coefficient: 80 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:01 (44 iterations)\u001b[39m8)\u001b[39mm\n",
      "\u001b[32msimulating aggregate path ... 73%|████████████████      |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 2.7407118780020667e-7 and B = [0.146874, 0.960939, 0.132891, 0.963317]\n",
      " --- Iteration over ALM coefficient: 81 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:01 (41 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 71%|████████████████      |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 2.485447657130102e-7 and B = [0.146874, 0.960939, 0.132891, 0.963317]\n",
      " --- Iteration over ALM coefficient: 82 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:01 (39 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 72%|████████████████      |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 2.250508124301831e-7 and B = [0.146874, 0.960939, 0.132891, 0.963317]\n",
      " --- Iteration over ALM coefficient: 83 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:01 (37 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 95%|█████████████████████ |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 2.0398252301156994e-7 and B = [0.146874, 0.960939, 0.132892, 0.963317]\n",
      " --- Iteration over ALM coefficient: 84 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:01 (35 iterations)\u001b[39m8)\u001b[39mm\n",
      "\u001b[32msimulating aggregate path ... 74%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.8501803553117036e-7 and B = [0.146874, 0.960939, 0.132892, 0.963317]\n",
      " --- Iteration over ALM coefficient: 85 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:01 (33 iterations)\u001b[39m8)\u001b[39mm\n",
      "\u001b[32msimulating aggregate path ... 74%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.6791167115060013e-7 and B = [0.146874, 0.960939, 0.132892, 0.963317]\n",
      " --- Iteration over ALM coefficient: 86 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:01 (32 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 72%|████████████████      |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.51750390336991e-7 and B = [0.146874, 0.960939, 0.132892, 0.963317]\n",
      " --- Iteration over ALM coefficient: 87 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:01 (30 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 75%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.3786970365359252e-7 and B = [0.146874, 0.960939, 0.132892, 0.963317]\n",
      " --- Iteration over ALM coefficient: 88 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:01 (28 iterations)\u001b[39m8)\u001b[39mm\n",
      "\u001b[32msimulating aggregate path ... 75%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.2521602682502575e-7 and B = [0.146874, 0.960939, 0.132892, 0.963317]\n",
      " --- Iteration over ALM coefficient: 89 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:01 (27 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 72%|████████████████      |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.1326223903207477e-7 and B = [0.146874, 0.960939, 0.132892, 0.963317]\n",
      " --- Iteration over ALM coefficient: 90 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:01 (25 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 72%|████████████████      |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.0293103455683017e-7 and B = [0.146874, 0.960939, 0.132892, 0.963317]\n",
      " --- Iteration over ALM coefficient: 91 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:01 (24 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 75%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 9.30346010230565e-8 and B = [0.146874, 0.960939, 0.132892, 0.963317]\n",
      " --- Iteration over ALM coefficient: 92 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:01 (22 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 72%|████████████████      |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 8.464245168648965e-8 and B = [0.146874, 0.960939, 0.132892, 0.963317]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[32msimulating aggregate path ... 96%|██████████████████████|  ETA: 0:00:00\u001b[39m\r",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " --- Iteration over ALM coefficient: 93 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:00 (21 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 72%|████████████████      |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 7.652786657152966e-8 and B = [0.146874, 0.960939, 0.132892, 0.963317]\n",
      " --- Iteration over ALM coefficient: 94 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:00 (19 iterations)\u001b[39m8)\u001b[39mm\n",
      "\u001b[32msimulating aggregate path ... 74%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 6.974838753781576e-8 and B = [0.146874, 0.960939, 0.132892, 0.963317]\n",
      " --- Iteration over ALM coefficient: 95 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:00 (19 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 76%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 6.263902901926244e-8 and B = [0.146874, 0.960939, 0.132892, 0.963317]\n",
      " --- Iteration over ALM coefficient: 96 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:00 (17 iterations)\u001b[39m8)\u001b[39mm\n",
      "\u001b[32msimulating aggregate path ... 96%|██████████████████████|  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 5.709994851943456e-8 and B = [0.146874, 0.960939, 0.132892, 0.963317]\n",
      " --- Iteration over ALM coefficient: 97 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:00 (16 iterations)\u001b[39m8)\u001b[39mm\n",
      "\u001b[32msimulating aggregate path ... 73%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 5.174000020091718e-8 and B = [0.146874, 0.960939, 0.132892, 0.963317]\n",
      " --- Iteration over ALM coefficient: 98 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:00 (15 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 95%|█████████████████████ |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 4.6844407131096943e-8 and B = [0.146874, 0.960939, 0.132892, 0.963317]\n",
      " --- Iteration over ALM coefficient: 99 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:00 (14 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 72%|████████████████      |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 4.254244148627073e-8 and B = [0.146874, 0.960939, 0.132892, 0.963317]\n",
      " --- Iteration over ALM coefficient: 100 ---"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[32msimulating aggregate path ... 96%|██████████████████████|  ETA: 0:00:00\u001b[39m\r",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:00 (14 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 73%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 3.804459813849803e-8 and B = [0.146874, 0.960939, 0.132892, 0.963317]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[32msimulating aggregate path ... 97%|██████████████████████|  ETA: 0:00:00\u001b[39m\r",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- Iteration over ALM coefficient: 101 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:00 (12 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 74%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 3.4976016666243837e-8 and B = [0.146874, 0.960939, 0.132892, 0.963317]\n",
      " --- Iteration over ALM coefficient: 102 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:00 (12 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 73%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 3.1486846935324664e-8 and B = [0.146874, 0.960939, 0.132892, 0.963317]\n",
      " --- Iteration over ALM coefficient: 103 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:00 (10 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 93%|█████████████████████ |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 2.904139495552016e-8 and B = [0.146874, 0.960939, 0.132892, 0.963317]\n",
      " --- Iteration over ALM coefficient: 104 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:00 (11 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 72%|████████████████      |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 2.5656462981915595e-8 and B = [0.146874, 0.960939, 0.132892, 0.963317]\n",
      " --- Iteration over ALM coefficient: 105 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:00 (9 iterations)\u001b[39m-08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 75%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 2.3675274851697736e-8 and B = [0.146874, 0.960939, 0.132892, 0.963317]\n",
      " --- Iteration over ALM coefficient: 106 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:00 (9 iterations)\u001b[39m-08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 94%|█████████████████████ |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 2.1270876116030735e-8 and B = [0.146874, 0.960939, 0.132892, 0.963317]\n",
      " --- Iteration over ALM coefficient: 107 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:00 (8 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 73%|████████████████      |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.9416610569367165e-8 and B = [0.146874, 0.960939, 0.132892, 0.963317]\n",
      " --- Iteration over ALM coefficient: 108 ---"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[32msimulating aggregate path ... 97%|██████████████████████|  ETA: 0:00:00\u001b[39m\r",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:00 (8 iterations)\u001b[39m-08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 73%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.7361788201553097e-8 and B = [0.146874, 0.960939, 0.132892, 0.963317]\n",
      " --- Iteration over ALM coefficient: 109 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:00 (7 iterations)\u001b[39m08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 94%|█████████████████████ |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.5907071271303863e-8 and B = [0.146874, 0.960939, 0.132892, 0.963317]\n",
      " --- Iteration over ALM coefficient: 110 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:00 (6 iterations)\u001b[39m-08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 87%|████████████████████  |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.468659144299167e-8 and B = [0.146874, 0.960939, 0.132892, 0.963317]\n",
      " --- Iteration over ALM coefficient: 111 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:00 (6 iterations)\u001b[39m-08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 72%|████████████████      |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.318548634166028e-8 and B = [0.146874, 0.960939, 0.132892, 0.963317]\n",
      " --- Iteration over ALM coefficient: 112 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by Euler method:  Time: 0:00:00 (6 iterations)\u001b[39m-08)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 74%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.1777319147698861e-8 and B = [0.146874, 0.960939, 0.132892, 0.963317]\n",
      " --- Iteration over ALM coefficient: 113 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 72%|████████████████      |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.078307437785142e-8 and B = [0.146874, 0.960939, 0.132892, 0.963317]\n",
      " --- Iteration over ALM coefficient: 114 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 74%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 9.680553608726328e-9 and B = [0.146874, 0.960939, 0.132892, 0.963317]\n",
      "-----------------------------------------------------\n",
      "ALM coefficient successfully converged : dif = 9.680553608726328e-9\n",
      "-----------------------------------------------------\n",
      "1273.846153 seconds (7.95 G allocations: 1.278 TiB, 15.51% gc time)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[32msimulating aggregate path ... 99%|██████████████████████|  ETA: 0:00:00\u001b[39m\r",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n"
     ]
    }
   ],
   "source": [
    "kss = KSSolution(ksp, load_value=false, load_B=false)\n",
    "ns = NonStochastic(ksp, zi_shocks[1])\n",
    "@time K_ts = find_ALM_coef!(EulerMethod(), \n",
    "            ns, ksp, kss, zi_shocks, \n",
    "            tol_ump = 1e-8, max_iter_ump = 10000,\n",
    "            tol_B = 1e-8, max_iter_B = 500, update_B = 0.3,\n",
    "            T_discard = T_discard);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_ALM(ksp.z_grid, zi_shocks, kss.B,K_ts, T_discard = T_discard)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Approximated aggregate capital law of motion\n",
      "log(K_{t+1})=0.1468742297741828+0.9609390142949115log(K_{t}) in good time (R2 = 0.9999985643122742)\n",
      "log(K_{t+1})=0.1328919612162856+0.9633166115209159log(K_{t}) in bad time (R2 = 0.9999976186272014)\n"
     ]
    }
   ],
   "source": [
    "#kss.B  # Regression coefficient\n",
    "println(\"Approximated aggregate capital law of motion\")\n",
    "println(\"log(K_{t+1})=$(kss.B[1])+$(kss.B[2])log(K_{t}) in good time (R2 = $(kss.R2[1]))\")\n",
    "println(\"log(K_{t+1})=$(kss.B[3])+$(kss.B[4])log(K_{t}) in bad time (R2 = $(kss.R2[2]))\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "@save \"result_Young.jld2\" ksp kss "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean of capital implied by regression is 40.1009518847617\n",
      "mean of capital implied by simulation is 40.350045260575776\n"
     ]
    }
   ],
   "source": [
    "# Compute mean of capital implied by regression\n",
    "mc=MarkovChain(ksp.transmat.Pz)\n",
    "sd=stationary_distributions(mc)[1]\n",
    "logKg=kss.B[1]/(1-kss.B[2])\n",
    "logKb=kss.B[3]/(1-kss.B[4])\n",
    "meanK_reg=exp(sd[1]*logKg+sd[2]*logKb)\n",
    "meanK_sim=mean(K_ts[T_discard+1:end])\n",
    "println(\"mean of capital implied by regression is $meanK_reg\")\n",
    "println(\"mean of capital implied by simulation is $meanK_sim\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figures in Krusell-Smith"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_Fig1(ksp ,kss, K_ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_Fig2(ksp, kss, 40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solution with value function iteration\n",
    "In this section, each agent's utility maximization problem is solved by value function iteration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's skip the following steps in this section to save computational time.\n",
    "- consturuction of `ksp` instance since it is same\n",
    "- consturuction of `kss` instance to use the previous result as initial guess of the solution\n",
    "- draws of the shocks to use same ones\n",
    "\n",
    "However, instead of constructing `kss` again, obtain value from the policy function derived by Euler method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "iterate_policy!(ksp, kss, n_iter=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Young's non-stochastic method is used for simuation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " --- Iteration over ALM coefficient: 1 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  (thresh = 1e-08, value = 1.0106e-08)\u001b[39mm"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ** VFI converged successfully!! dif = 9.994721494877012e-9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  Time: 0:07:55 (1736 iterations)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 83%|███████████████████   |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.428805175480162 and B = [0.428805, 0.884554, 0.410498, 0.887896]\n",
      " --- Iteration over ALM coefficient: 2 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by VFI:  (thresh = 1e-08, value = 1.00532e-08)\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ** VFI converged successfully!! dif = 9.939441270034877e-9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  Time: 0:05:29 (1256 iterations)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 82%|██████████████████    |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.014460894014056674 and B = [0.143102, 0.961781, 0.114063, 0.96817]\n",
      " --- Iteration over ALM coefficient: 3 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by VFI:  (thresh = 1e-08, value = 1.00066e-08)\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ** VFI converged successfully!! dif = 9.893028618535027e-9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  Time: 0:05:17 (1188 iterations)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 89%|████████████████████  |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.011577595533557777 and B = [0.144557, 0.961503, 0.119452, 0.966854]\n",
      " --- Iteration over ALM coefficient: 4 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by VFI:  (thresh = 1e-08, value = 1.00553e-08)\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ** VFI converged successfully!! dif = 9.941516054823296e-9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  Time: 0:04:34 (1003 iterations)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 82%|███████████████████   |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.00672525014133743 and B = [0.143178, 0.96185, 0.118426, 0.967128]\n",
      " --- Iteration over ALM coefficient: 5 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by VFI:  (thresh = 1e-08, value = 1.00691e-08)\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ** VFI converged successfully!! dif = 9.952884738595458e-9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  Time: 0:03:35 (808 iterations)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 80%|██████████████████    |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.0041543727804939645 and B = [0.142625, 0.961995, 0.118337, 0.967161]\n",
      " --- Iteration over ALM coefficient: 6 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by VFI:  (thresh = 1e-08, value = 1.0075e-08)\u001b[39mm"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ** VFI converged successfully!! dif = 9.96114124518499e-9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  Time: 0:02:60 (686 iterations)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 83%|███████████████████   |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.0025803524375948572 and B = [0.142297, 0.962079, 0.118271, 0.967184]\n",
      " --- Iteration over ALM coefficient: 7 ---"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  (thresh = 1e-08, value = 1.03562e-08)\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ** VFI converged successfully!! dif = 9.966854008780501e-9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  Time: 0:02:47 (642 iterations)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 83%|███████████████████   |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.0016218195891860299 and B = [0.142113, 0.962125, 0.118257, 0.967191]\n",
      " --- Iteration over ALM coefficient: 8 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by VFI:  (thresh = 1e-08, value = 1.00832e-08)\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ** VFI converged successfully!! dif = 9.961809155356605e-9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  Time: 0:03:10 (605 iterations)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 89%|████████████████████  |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.0010273799823305452 and B = [0.142005, 0.962152, 0.118259, 0.967192]\n",
      " --- Iteration over ALM coefficient: 9 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by VFI:  (thresh = 1e-08, value = 1.01004e-08)\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ** VFI converged successfully!! dif = 9.978975867852569e-9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  Time: 0:02:54 (573 iterations)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 93%|█████████████████████ |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.0006554489272417308 and B = [0.141941, 0.962168, 0.118267, 0.967192]\n",
      " --- Iteration over ALM coefficient: 10 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:03\u001b[39m\n",
      "\u001b[32mSolving individual UMP by VFI:  (thresh = 1e-08, value = 1.01048e-08)\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ** VFI converged successfully!! dif = 9.96641347228433e-9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  Time: 0:02:47 (539 iterations)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 80%|██████████████████    |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.00042016726753904554 and B = [0.141903, 0.962178, 0.118276, 0.96719]\n",
      " --- Iteration over ALM coefficient: 11 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by VFI:  (thresh = 1e-08, value = 1.01166e-08)\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ** VFI converged successfully!! dif = 9.994266747526126e-9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  Time: 0:02:31 (503 iterations)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 77%|█████████████████     |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.0002703569084139268 and B = [0.141879, 0.962184, 0.118283, 0.967189]\n",
      " --- Iteration over ALM coefficient: 12 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by VFI:  (thresh = 1e-08, value = 1.0025e-08)\u001b[39mm"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ** VFI converged successfully!! dif = 9.903544651024276e-9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  Time: 0:02:25 (471 iterations)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 70%|████████████████      |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.0001747881005138563 and B = [0.141864, 0.962187, 0.118289, 0.967188]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[32msimulating aggregate path ... 95%|█████████████████████ |  ETA: 0:00:00\u001b[39m\r",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " --- Iteration over ALM coefficient: 13 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  (thresh = 1e-08, value = 1.01129e-08)\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ** VFI converged successfully!! dif = 9.989889804273844e-9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  Time: 0:02:01 (436 iterations)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 79%|██████████████████    |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 0.00011310615847315297 and B = [0.141855, 0.962189, 0.118293, 0.967187]\n",
      " --- Iteration over ALM coefficient: 14 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by VFI:  (thresh = 1e-08, value = 1.00514e-08)\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ** VFI converged successfully!! dif = 9.928498911904171e-9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  Time: 0:01:56 (402 iterations)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 93%|█████████████████████ |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 7.32621346145601e-5 and B = [0.141849, 0.962191, 0.118296, 0.967186]\n",
      " --- Iteration over ALM coefficient: 15 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by VFI:  (thresh = 1e-08, value = 1.00943e-08)\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ** VFI converged successfully!! dif = 9.970108294510283e-9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  Time: 0:01:42 (368 iterations)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 74%|█████████████████     |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 4.770718960114828e-5 and B = [0.141846, 0.962191, 0.118298, 0.967186]\n",
      " --- Iteration over ALM coefficient: 16 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by VFI:  (thresh = 1e-08, value = 1.00158e-08)\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ** VFI converged successfully!! dif = 9.891550689644646e-9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  Time: 0:01:57 (334 iterations)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 91%|█████████████████████ |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 3.090272309547171e-5 and B = [0.141843, 0.962192, 0.1183, 0.967186]\n",
      " --- Iteration over ALM coefficient: 17 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by VFI:  (thresh = 1e-08, value = 1.0051e-08)\u001b[39mm"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ** VFI converged successfully!! dif = 9.926452548825182e-9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  Time: 0:01:29 (308 iterations)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 81%|██████████████████    |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 2.0087004701296873e-5 and B = [0.141842, 0.962192, 0.1183, 0.967186]\n",
      " --- Iteration over ALM coefficient: 18 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:03\u001b[39m\n",
      "\u001b[32mSolving individual UMP by VFI:  (thresh = 1e-08, value = 1.01298e-08)\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ** VFI converged successfully!! dif = 9.999837402574485e-9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  Time: 0:01:18 (258 iterations)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 79%|██████████████████    |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.3064436913179378e-5 and B = [0.141841, 0.962193, 0.118301, 0.967185]\n",
      " --- Iteration over ALM coefficient: 19 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by VFI:  (thresh = 1e-08, value = 1.00138e-08)\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ** VFI converged successfully!! dif = 9.88967485682224e-9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  Time: 0:01:17 (255 iterations)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 72%|████████████████      |  ETA: 0:00:01\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 8.69738075323645e-6 and B = [0.14184, 0.962193, 0.118302, 0.967185]\n",
      " --- Iteration over ALM coefficient: 20 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by VFI:  (thresh = 1e-08, value = 1.00476e-08)\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ** VFI converged successfully!! dif = 9.919460808305303e-9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  Time: 0:01:34 (206 iterations)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 89%|████████████████████  |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 5.468240175970918e-6 and B = [0.14184, 0.962193, 0.118302, 0.967185]\n",
      " --- Iteration over ALM coefficient: 21 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by VFI:  (thresh = 1e-08, value = 1.01033e-08)\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ** VFI converged successfully!! dif = 9.976076853490667e-9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  Time: 0:00:55 (174 iterations)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 76%|█████████████████     |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 3.580045910361873e-6 and B = [0.141839, 0.962193, 0.118302, 0.967185]\n",
      " --- Iteration over ALM coefficient: 22 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by VFI:  (thresh = 1e-08, value = 1.00782e-08)\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ** VFI converged successfully!! dif = 9.948990964403492e-9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  Time: 0:00:44 (135 iterations)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 79%|██████████████████    |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 2.2968971356840395e-6 and B = [0.141839, 0.962193, 0.118302, 0.967185]\n",
      " --- Iteration over ALM coefficient: 23 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by VFI:  (thresh = 1e-08, value = 1.00779e-08)\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ** VFI converged successfully!! dif = 9.9573185252666e-9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  Time: 0:00:45 (161 iterations)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 80%|██████████████████    |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.520622740197597e-6 and B = [0.141839, 0.962193, 0.118302, 0.967185]\n",
      " --- Iteration over ALM coefficient: 24 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by VFI:  (thresh = 1e-08, value = 1.02691e-08)\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ** VFI converged successfully!! dif = 9.996881544793723e-9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  Time: 0:00:22 (85 iterations)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 80%|██████████████████    |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 9.749422733851265e-7 and B = [0.141839, 0.962193, 0.118302, 0.967185]\n",
      " --- Iteration over ALM coefficient: 25 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by VFI:  (thresh = 1e-08, value = 1.01489e-08)\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ** VFI converged successfully!! dif = 9.94174342849874e-9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  Time: 0:00:19 (72 iterations)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 81%|██████████████████    |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 6.30561285019926e-7 and B = [0.141839, 0.962193, 0.118302, 0.967185]\n",
      " --- Iteration over ALM coefficient: 26 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by VFI:  (thresh = 1e-08, value = 1.00036e-08)\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ** VFI converged successfully!! dif = 9.854176141743665e-9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  Time: 0:00:24 (91 iterations)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 80%|██████████████████    |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 4.689234013866006e-7 and B = [0.141839, 0.962193, 0.118302, 0.967185]\n",
      " --- Iteration over ALM coefficient: 27 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by VFI:  (thresh = 1e-08, value = 1.03537e-08)\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ** VFI converged successfully!! dif = 9.96635662886547e-9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  Time: 0:00:15 (57 iterations)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 76%|█████████████████     |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 3.1492881336081524e-7 and B = [0.141839, 0.962193, 0.118302, 0.967185]\n",
      " --- Iteration over ALM coefficient: 28 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by VFI:  (thresh = 1e-08, value = 1.00153e-08)\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ** VFI converged successfully!! dif = 9.578542403687607e-9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  Time: 0:00:13 (50 iterations)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 81%|██████████████████    |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.5169019318417298e-7 and B = [0.141839, 0.962193, 0.118302, 0.967185]\n",
      " --- Iteration over ALM coefficient: 29 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by VFI:  (thresh = 1e-08, value = 1.01037e-08)\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ** VFI converged successfully!! dif = 9.968800895876484e-9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  Time: 0:00:21 (69 iterations)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 79%|██████████████████    |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.705320638450969e-7 and B = [0.141839, 0.962193, 0.118302, 0.967185]\n",
      " --- Iteration over ALM coefficient: 30 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by VFI:  (thresh = 1e-08, value = 1.0077e-08)\u001b[39mm"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ** VFI converged successfully!! dif = 9.860116279014619e-9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  Time: 0:00:11 (43 iterations)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 82%|██████████████████    |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 1.3195634965268432e-7 and B = [0.141839, 0.962193, 0.118302, 0.967185]\n",
      " --- Iteration over ALM coefficient: 31 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by VFI:  (thresh = 1e-08, value = 1.03134e-08)\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ** VFI converged successfully!! dif = 9.833968306338647e-9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  Time: 0:00:08 (30 iterations)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 80%|██████████████████    |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 4.341085224623953e-8 and B = [0.141839, 0.962193, 0.118302, 0.967185]\n",
      " --- Iteration over ALM coefficient: 32 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by VFI:  (thresh = 1e-08, value = 1.02358e-08)\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ** VFI converged successfully!! dif = 9.643912335377536e-9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  Time: 0:00:05 (21 iterations)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 80%|██████████████████    |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 4.6830600647362886e-8 and B = [0.141839, 0.962193, 0.118302, 0.967185]\n",
      " --- Iteration over ALM coefficient: 33 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n",
      "\u001b[32mSolving individual UMP by VFI:  (thresh = 1e-08, value = 1.01893e-08)\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ** VFI converged successfully!! dif = 9.76172032096656e-9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mSolving individual UMP by VFI:  Time: 0:00:09 (33 iterations)\u001b[39m\n",
      "\u001b[32msimulating aggregate path ... 78%|██████████████████    |  ETA: 0:00:00\u001b[39m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of ALM coefficient is 6.680717812290737e-9 and B = [0.141839, 0.962193, 0.118302, 0.967185]\n",
      "-----------------------------------------------------\n",
      "ALM coefficient successfully converged : dif = 6.680717812290737e-9\n",
      "-----------------------------------------------------\n",
      "3935.643705 seconds (29.98 G allocations: 5.733 TiB, 16.24% gc time)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[32msimulating aggregate path ...100%|██████████████████████| Time: 0:00:02\u001b[39m\n"
     ]
    }
   ],
   "source": [
    "kss = KSSolution(ksp, load_value=false, load_B=false)\n",
    "ns = NonStochastic(ksp, zi_shocks[1])\n",
    "@time K_ts = find_ALM_coef!(VFI(Howard_on=false, Howard_n_iter=20),\n",
    "                ns, ksp, kss, zi_shocks, \n",
    "            tol_ump = 1e-8, max_iter_ump = 10000,\n",
    "            tol_B = 1e-8, max_iter_B = 500, update_B = 0.3,\n",
    "            T_discard = T_discard);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_ALM(ksp.z_grid, zi_shocks, kss.B, K_ts, T_discard = T_discard)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Approximated aggregate capital law of motion\n",
      "log(K_{t+1})=0.14183884897222415+0.962193057333297log(K_{t}) in good time (R2 = 0.9999993082093952)\n",
      "log(K_{t+1})=0.11830233274973227+0.9671851357222669log(K_{t}) in bad time (R2 = 0.9999989625914406)\n"
     ]
    }
   ],
   "source": [
    "#kss.B  # Regression coefficient\n",
    "println(\"Approximated aggregate capital law of motion\")\n",
    "println(\"log(K_{t+1})=$(kss.B[1])+$(kss.B[2])log(K_{t}) in good time (R2 = $(kss.R2[1]))\")\n",
    "println(\"log(K_{t+1})=$(kss.B[3])+$(kss.B[4])log(K_{t}) in bad time (R2 = $(kss.R2[2]))\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "@save \"result_VFI.jld2\" ksp kss "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean of capital implied by regression is 39.58313131174572\n",
      "mean of capital implied by simulation is 39.94733411715162\n"
     ]
    }
   ],
   "source": [
    "# Compute mean of capital implied by regression\n",
    "mc = MarkovChain(ksp.transmat.Pz)\n",
    "sd = stationary_distributions(mc)[1]\n",
    "logKg = kss.B[1]/(1-kss.B[2])\n",
    "logKb = kss.B[3]/(1-kss.B[4])\n",
    "meanK_reg = exp(sd[1]*logKg + sd[2]*logKb)\n",
    "meanK_sim = mean(K_ts[T_discard+1:end])\n",
    "println(\"mean of capital implied by regression is $meanK_reg\")\n",
    "println(\"mean of capital implied by simulation is $meanK_sim\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figures in Krusell-Smith"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_Fig1(ksp, kss, K_ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_Fig2(ksp, kss, 40)"
   ]
  }
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