empirical_application_network_centrality_trade.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Empirical application I: estimating a trade network\n",
    "\n",
    "We are taking the data set used in Bykhovskaya (2019) to model the network of trade in pharmaceuticals among the twelve original European Union member states. We will take first differences across time and estimate the network of average growth rates. Based on this noisy estimate, we find coefficient-wise confidence intervals for the estimated network centralities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "using TimeSeries, Distributions, Statistics, LinearAlgebra, CSV, DataFrames, Random, Colors\n",
    "trade_flows_vectorised = DataFrame(CSV.File(\"data/30euro_clean.csv\";header=false));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert this table to a sequence of adjacency matrices. To be able to abstract away from missing data issues, we start the sample in November 2004 until February 2018. This reduction provides us with a sample of length $T=161$ for a $7 \\times 7$ network. We estimate the adjacency matrix $M$ by $\\hat{M}$ as in Simons (2023). In particular, we average the first differences of the natural logarithm, which provides an estimate of the average growth rate. To account for potential serial correlation in the errors, we use a HAC estimator to estimate a robust covariance matrix. We interpret the nodes in the directed graph that have on average grown their trade balance the most and with most connections as the most central actors in the trade network and estimate eigenvector centralities and provide one-sided confidence intervals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_start = 70;\n",
    "T = size(trade_flows_vectorised)[2]\n",
    "trade_matrices = zeros(12,12,T-t_start+1);\n",
    "trade_matrices_wm = zeros(Union{Missing,Float64},12,12,T-t_start+1);\n",
    "trade_matrices_wm_fd = zeros(Union{Missing,Float64},12,12,T-t_start+1);\n",
    "\n",
    "for t in t_start:T\n",
    "    k=1;\n",
    "    for i in 1:12\n",
    "        indices = filter!(e->e≠i,collect(1:12))\n",
    "        for j in indices\n",
    "            #don't touch missing values\n",
    "            trade_matrices_wm[i,j,t-t_start+1] = trade_flows_vectorised[k,t];\n",
    "            \n",
    "            if Missing == typeof(trade_flows_vectorised[k,t])\n",
    "                #fill in zeros for missing values\n",
    "                trade_matrices[i,j,t-t_start+1] = 0;\n",
    "            else\n",
    "                #non-missing observation\n",
    "                trade_matrices[i,j,t-t_start+1] = trade_flows_vectorised[k,t];\n",
    "            end            \n",
    "            k += 1;\n",
    "        end #j\n",
    "    end #i\n",
    "end #t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Construct a table of node labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>12×2 DataFrame</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">NodeOld</th><th style = \"text-align: left;\">Country</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"String\" style = \"text-align: left;\">String</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: right;\">1</td><td style = \"text-align: left;\">FR</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: right;\">2</td><td style = \"text-align: left;\">NL</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: right;\">3</td><td style = \"text-align: left;\">DE</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">4</td><td style = \"text-align: right;\">4</td><td style = \"text-align: left;\">IT</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">5</td><td style = \"text-align: right;\">5</td><td style = \"text-align: left;\">UK</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">6</td><td style = \"text-align: right;\">6</td><td style = \"text-align: left;\">IE</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">7</td><td style = \"text-align: right;\">7</td><td style = \"text-align: left;\">DK</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">8</td><td style = \"text-align: right;\">8</td><td style = \"text-align: left;\">EL</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">9</td><td style = \"text-align: right;\">9</td><td style = \"text-align: left;\">PT</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">10</td><td style = \"text-align: right;\">10</td><td style = \"text-align: left;\">ES</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">11</td><td style = \"text-align: right;\">11</td><td style = \"text-align: left;\">BE</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">12</td><td style = \"text-align: right;\">12</td><td style = \"text-align: left;\">LU</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& NodeOld & Country\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & String\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & FR \\\\\n",
       "\t2 & 2 & NL \\\\\n",
       "\t3 & 3 & DE \\\\\n",
       "\t4 & 4 & IT \\\\\n",
       "\t5 & 5 & UK \\\\\n",
       "\t6 & 6 & IE \\\\\n",
       "\t7 & 7 & DK \\\\\n",
       "\t8 & 8 & EL \\\\\n",
       "\t9 & 9 & PT \\\\\n",
       "\t10 & 10 & ES \\\\\n",
       "\t11 & 11 & BE \\\\\n",
       "\t12 & 12 & LU \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m12×2 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m NodeOld \u001b[0m\u001b[1m Country \u001b[0m\n",
       "     │\u001b[90m Int64   \u001b[0m\u001b[90m String  \u001b[0m\n",
       "─────┼──────────────────\n",
       "   1 │       1  FR\n",
       "   2 │       2  NL\n",
       "   3 │       3  DE\n",
       "   4 │       4  IT\n",
       "   5 │       5  UK\n",
       "   6 │       6  IE\n",
       "   7 │       7  DK\n",
       "   8 │       8  EL\n",
       "   9 │       9  PT\n",
       "  10 │      10  ES\n",
       "  11 │      11  BE\n",
       "  12 │      12  LU"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "using DataFrames\n",
    "country_labels = DataFrame(NodeOld = 1:12,Country = [\"FR\",\"NL\",\"DE\",\"IT\",\"UK\",\"IE\",\"DK\",\"EL\",\"PT\",\"ES\",\"BE\",\"LU\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check which nodes have missing values as the first observation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First observation for pair [12, 6] is missing\n",
      "First observation for pair [7, 12] is missing\n",
      "First observation for pair [8, 10] is missing\n",
      "Nodes to eliminate"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12, 7, 8, 6, 10]\n"
     ]
    }
   ],
   "source": [
    "missingmatrix = zeros(12,12);\n",
    "\n",
    "for i in 1:12, j in i+1:12\n",
    "    missingmatrix[i,j] = length(findall(ismissing.(trade_matrices_wm[i,j,:])));\n",
    "    missingmatrix[j,i] = length(findall(ismissing.(trade_matrices_wm[j,i,:])));\n",
    "end\n",
    "\n",
    "first_entry_missing = zeros(Bool,12,12)\n",
    "pairs_w_first_entry_missing = []\n",
    "pairs_w_first_entry_notmissing = []\n",
    "\n",
    "for i in 1:12, j in i+1:12\n",
    "    first_entry_missing[i,j] = 1 == findfirst(ismissing.(trade_matrices_wm[i,j,:]));\n",
    "    first_entry_missing[j,i] = 1 == findfirst(ismissing.(trade_matrices_wm[j,i,:]));\n",
    "    if 1 == findfirst(ismissing.(trade_matrices_wm[i,j,:]))\n",
    "        push!(pairs_w_first_entry_missing,[i j]) else\n",
    "        push!(pairs_w_first_entry_notmissing,[i j])\n",
    "    end;\n",
    "    if 1 == findfirst(ismissing.(trade_matrices_wm[j,i,:]))\n",
    "        push!(pairs_w_first_entry_missing,[j i]) else\n",
    "        push!(pairs_w_first_entry_notmissing,[j i])\n",
    "    end;\n",
    "end\n",
    "l = length(pairs_w_first_entry_missing)\n",
    "#recall that reshape goes through the dimensions in order so exhausts the first dimension then the second etc.\n",
    "pairs_to_eliminate = reshape(stack(pairs_w_first_entry_missing),(2,l))'\n",
    "pairs_to_keep = reshape(stack(pairs_w_first_entry_notmissing),(2,length(pairs_w_first_entry_notmissing)))'\n",
    "for row in eachrow(pairs_to_eliminate)\n",
    "    println(\"First observation for pair [\",row[1],\", \",row[2],\"] is \",trade_matrices_wm[row[1],row[2],1])\n",
    "end\n",
    "nodes_to_eliminate = unique(pairs_to_eliminate);\n",
    "println(\"Nodes to eliminate\",nodes_to_eliminate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eliminate nodes that have a missing value as the first observation and check that taking logarithms and first differences does not produce `NaN` or `Inf` entries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "p=12-length(nodes_to_eliminate);\n",
    "trade_matrices_cf = zeros(Float64,p,p,T-t_start+1) #carried forward for missing values\n",
    "\n",
    "k=0;\n",
    "locf(v) = v[accumulate(max,[i*!ismissing(v[i]) for i=1:length(v)])]\n",
    "row_indices = filter!(e -> e ∉(nodes_to_eliminate),collect(1:12))\n",
    "node_hash_map = zeros(length(row_indices),2)\n",
    "for i in enumerate(row_indices)\n",
    "    node_hash_map[i[1],1] = i[2]; #put the old node in\n",
    "    node_hash_map[i[1],2] = i[1]; #put the new node in\n",
    "    col_indices = filter!(e -> e ≠ i && e ∉(nodes_to_eliminate),collect(1:12))\n",
    "    for j in enumerate(col_indices)\n",
    "        trade_matrices_cf[i[1],j[1],:] = locf(trade_matrices_wm[i[2],j[2],:])\n",
    "    end #j\n",
    "end #i\n",
    "\n",
    "#find first differences\n",
    "trade_matrices_cf_fd = zeros(Float64,p,p,T-t_start+1-1)\n",
    "\n",
    "function simple_check(x)\n",
    "    any(isnan.(x)) || any(.!isfinite.(x))\n",
    "end\n",
    "\n",
    "for i in 1:p, j in i+1:p\n",
    "    trade_matrices_cf_fd[i,j,:] = diff(log.(trade_matrices_cf[i,j,:]));\n",
    "    trade_matrices_cf_fd[j,i,:] = diff(log.(trade_matrices_cf[j,i,:]));\n",
    "    if simple_check(trade_matrices_cf_fd[i,j,:]) println(\"Nan or Inf at \",i,\" \",j) end\n",
    "    if simple_check(trade_matrices_cf_fd[j,i,:]) println(\"Nan or Inf at \",j,\" \",i) end\n",
    "end\n",
    "\n",
    "node_conversion_table = DataFrame(  NodeOld = node_hash_map[:,1],\n",
    "            NodeNew = node_hash_map[:,2]);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimate centralities based on average growth rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "using CovarianceMatrices\n",
    "include(\"eigenvector_estimates_cis.jl\")\n",
    "p = size(trade_matrices_cf_fd)[1];\n",
    "T = size(trade_matrices_cf_fd)[3];\n",
    "vectorised_matrices = reshape(trade_matrices_cf_fd,(p^2,T));\n",
    "y = vectorised_matrices'\n",
    "X   = ones(T,1)\n",
    "_,K = size(X)\n",
    "b   = X\\y\n",
    "res = y - X*b\n",
    "\n",
    "kernel = QuadraticSpectralKernel{NeweyWest}()\n",
    "bw = CovarianceMatrices.optimalbandwidth(kernel, res, prewhite=false);\n",
    "Ψ   = lrvar(QuadraticSpectralKernel(bw), res, scale = T^2/(T-K))   # df adjustment is built into vcov\n",
    "estimates_abs_tuple = eigenvector_estimates_cis_abs(trade_matrices_cf_fd ,1,0.05,false,Ψ);\n",
    "estimates_abs = estimates_abs_tuple[1]\n",
    "graph_estimate = estimates_abs_tuple[2];"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the array `estimates_abs` has the point estimates and upper bounds as the first two matrices and the value of the objective function for the finding of an upper bound as its third matrix. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7-element Vector{String}:\n",
       " \"PT\"\n",
       " \"DE\"\n",
       " \"NL\"\n",
       " \"UK\"\n",
       " \"IT\"\n",
       " \"FR\"\n",
       " \"BE\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#make a table for the eigenvector centralities\n",
    "eigenvector_centralities = DataFrame(   NodeOld = node_hash_map[:,1],\n",
    "                                        NodeNew = node_hash_map[:,2],\n",
    "                                        country = [\"A\",\"B\",\"C\",\"D\",\"E\",\"F\",\"G\"],\n",
    "                                        LogCentrality = log.(estimates_abs[:,1,1]),\n",
    "                                        LogCentrality_upper = log.(estimates_abs[:,1,2]),\n",
    "                                        LogCentrality_error = log.(estimates_abs[:,1,2]) - log.(estimates_abs[:,1,1]));\n",
    "eigenvector_centralities = leftjoin(eigenvector_centralities,country_labels,on = :NodeOld=>:NodeOld)\n",
    "sort!(eigenvector_centralities,:LogCentrality,rev=true)\n",
    "eigenvector_centralities[!,:Country] = convert.(String,eigenvector_centralities[!,:Country])\n",
    "#CSV.write(\"network_statistics.csv\",eigenvector_centralities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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       "</svg>\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "using Plots\n",
    "pgfplotsx()\n",
    "endrow = size(node_conversion_table)[1]-1\n",
    "centralities_plot = plot(   eigenvector_centralities[1:endrow,:Country],\n",
    "        eigenvector_centralities[1:endrow,:LogCentrality],\n",
    "        yerr = (zeros(endrow),\n",
    "                eigenvector_centralities[1:endrow,:LogCentrality_error]),\n",
    "        linetype=:scatter,label=\"\")\n",
    "centralities_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"/Users/jerome/Documents/resolvent-note/simulation-inference/plots/centrality_intervals.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "savefig(centralities_plot,\"plots/centrality_intervals.svg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualise the associated (average) graph of growth rates using a `SimpleWeightedDigraph` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#WARNING: both GraphMakie and GraphRecipes export \"graphplot\"; uses of it in module Main must be qualified\n",
    "using CairoMakie, GraphMakie, Graphs, SimpleWeightedGraphs, GraphMakie.NetworkLayout\n",
    "#use GLMakie for the interactive version\n",
    "#using Karnak, Graphs, SimpleWeightedGraphs, NetworkLayout, Colors\n",
    "g = SimpleWeightedDiGraph(p);\n",
    "width_vec = 1 .+ zeros(2*p);\n",
    "width_vec[1:7] =  ones(7);\n",
    "k=1\n",
    "for i in 1:p, j in i+1:p\n",
    "    add_edge!(g,i,j,graph_estimate[i,j]);\n",
    "    add_edge!(g,j,i,graph_estimate[j,i]);\n",
    "    #width_vec[k] = graph_estimate[i,j];\n",
    "    k += 1;\n",
    "    #width_vec[k] = graph_estimate[j,i];\n",
    "    k += 1;\n",
    "    #println(\"i \",i,\" j \",j,\" \",graph_estimate[i,j])\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot this again using only greyscale colours."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "using GraphRecipes\n",
    "pythonplot()\n",
    "c = exp.(eigenvector_centralities[:,:LogCentrality]);\n",
    "\n",
    "gpout_greyscale = GraphRecipes.graphplot(g,\n",
    "                        node_weights = c,\n",
    "                        names  = eigenvector_centralities[:,:Country],\n",
    "                        nodeshape = :circle,\n",
    "                        nodesize = .25,\n",
    "                        nodecolor = \"grey97\",\n",
    "                        markerstrokewidth=.5,\n",
    "                        edgewidth = (s,d,w) -> 200*graph_estimate[s,d],\n",
    "                        method=:sfdp)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"/Users/jerome/Documents/resolvent-note/simulation-inference/plots/graph_w_centralities_trade_raw_simple.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "savefig(gpout_greyscale,\"plots/graph_w_centralities_trade_raw_simple.svg\")"
   ]
  }
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