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bring precompile back
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@thorek1
thorek1 committed Sep 24, 2023
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Showing 1 changed file with 70 additions and 70 deletions.
140 changes: 70 additions & 70 deletions src/MacroModelling.jl
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Expand Up @@ -5228,79 +5228,79 @@ end



# @setup_workload begin
# # Putting some things in `setup` can reduce the size of the
# # precompile file and potentially make loading faster.
# @model FS2000 precompile = true begin
# dA[0] = exp(gam + z_e_a * e_a[x])
# log(m[0]) = (1 - rho) * log(mst) + rho * log(m[-1]) + z_e_m * e_m[x]
# - P[0] / (c[1] * P[1] * m[0]) + bet * P[1] * (alp * exp( - alp * (gam + log(e[1]))) * k[0] ^ (alp - 1) * n[1] ^ (1 - alp) + (1 - del) * exp( - (gam + log(e[1])))) / (c[2] * P[2] * m[1])=0
# W[0] = l[0] / n[0]
# - (psi / (1 - psi)) * (c[0] * P[0] / (1 - n[0])) + l[0] / n[0] = 0
# R[0] = P[0] * (1 - alp) * exp( - alp * (gam + z_e_a * e_a[x])) * k[-1] ^ alp * n[0] ^ ( - alp) / W[0]
# 1 / (c[0] * P[0]) - bet * P[0] * (1 - alp) * exp( - alp * (gam + z_e_a * e_a[x])) * k[-1] ^ alp * n[0] ^ (1 - alp) / (m[0] * l[0] * c[1] * P[1]) = 0
# c[0] + k[0] = exp( - alp * (gam + z_e_a * e_a[x])) * k[-1] ^ alp * n[0] ^ (1 - alp) + (1 - del) * exp( - (gam + z_e_a * e_a[x])) * k[-1]
# P[0] * c[0] = m[0]
# m[0] - 1 + d[0] = l[0]
# e[0] = exp(z_e_a * e_a[x])
# y[0] = k[-1] ^ alp * n[0] ^ (1 - alp) * exp( - alp * (gam + z_e_a * e_a[x]))
# gy_obs[0] = dA[0] * y[0] / y[-1]
# gp_obs[0] = (P[0] / P[-1]) * m[-1] / dA[0]
# log_gy_obs[0] = log(gy_obs[0])
# log_gp_obs[0] = log(gp_obs[0])
# end
@setup_workload begin
# Putting some things in `setup` can reduce the size of the
# precompile file and potentially make loading faster.
@model FS2000 precompile = true begin
dA[0] = exp(gam + z_e_a * e_a[x])
log(m[0]) = (1 - rho) * log(mst) + rho * log(m[-1]) + z_e_m * e_m[x]
- P[0] / (c[1] * P[1] * m[0]) + bet * P[1] * (alp * exp( - alp * (gam + log(e[1]))) * k[0] ^ (alp - 1) * n[1] ^ (1 - alp) + (1 - del) * exp( - (gam + log(e[1])))) / (c[2] * P[2] * m[1])=0
W[0] = l[0] / n[0]
- (psi / (1 - psi)) * (c[0] * P[0] / (1 - n[0])) + l[0] / n[0] = 0
R[0] = P[0] * (1 - alp) * exp( - alp * (gam + z_e_a * e_a[x])) * k[-1] ^ alp * n[0] ^ ( - alp) / W[0]
1 / (c[0] * P[0]) - bet * P[0] * (1 - alp) * exp( - alp * (gam + z_e_a * e_a[x])) * k[-1] ^ alp * n[0] ^ (1 - alp) / (m[0] * l[0] * c[1] * P[1]) = 0
c[0] + k[0] = exp( - alp * (gam + z_e_a * e_a[x])) * k[-1] ^ alp * n[0] ^ (1 - alp) + (1 - del) * exp( - (gam + z_e_a * e_a[x])) * k[-1]
P[0] * c[0] = m[0]
m[0] - 1 + d[0] = l[0]
e[0] = exp(z_e_a * e_a[x])
y[0] = k[-1] ^ alp * n[0] ^ (1 - alp) * exp( - alp * (gam + z_e_a * e_a[x]))
gy_obs[0] = dA[0] * y[0] / y[-1]
gp_obs[0] = (P[0] / P[-1]) * m[-1] / dA[0]
log_gy_obs[0] = log(gy_obs[0])
log_gp_obs[0] = log(gp_obs[0])
end

# @parameters FS2000 silent = true precompile = true begin
# alp = 0.356
# bet = 0.993
# gam = 0.0085
# mst = 1.0002
# rho = 0.129
# psi = 0.65
# del = 0.01
# z_e_a = 0.035449
# z_e_m = 0.008862
# end
@parameters FS2000 silent = true precompile = true begin
alp = 0.356
bet = 0.993
gam = 0.0085
mst = 1.0002
rho = 0.129
psi = 0.65
del = 0.01
z_e_a = 0.035449
z_e_m = 0.008862
end

# ENV["GKSwstype"] = "nul"

# @compile_workload begin
# # all calls in this block will be precompiled, regardless of whether
# # they belong to your package or not (on Julia 1.8 and higher)
# @model RBC precompile = true begin
# 1 / c[0] = (0.95 / c[1]) * (α * exp(z[1]) * k[0]^(α - 1) + (1 - δ))
# c[0] + k[0] = (1 - δ) * k[-1] + exp(z[0]) * k[-1]^α
# z[0] = 0.2 * z[-1] + 0.01 * eps_z[x]
# end
ENV["GKSwstype"] = "nul"

@compile_workload begin
# all calls in this block will be precompiled, regardless of whether
# they belong to your package or not (on Julia 1.8 and higher)
@model RBC precompile = true begin
1 / c[0] = (0.95 / c[1]) * (α * exp(z[1]) * k[0]^(α - 1) + (1 - δ))
c[0] + k[0] = (1 - δ) * k[-1] + exp(z[0]) * k[-1]^α
z[0] = 0.2 * z[-1] + 0.01 * eps_z[x]
end

# @parameters RBC silent = true precompile = true begin
# δ = 0.02
# α = 0.5
# end
@parameters RBC silent = true precompile = true begin
δ = 0.02
α = 0.5
end

# get_SS(FS2000)
# get_SS(FS2000, parameters = :alp => 0.36)
# get_solution(FS2000)
# get_solution(FS2000, parameters = :alp => 0.35)
# get_standard_deviation(FS2000)
# get_correlation(FS2000)
# get_autocorrelation(FS2000)
# get_variance_decomposition(FS2000)
# get_conditional_variance_decomposition(FS2000)
# get_irf(FS2000)

# data = simulate(FS2000)[:,:,1]
# observables = [:c,:k]
# calculate_kalman_filter_loglikelihood(FS2000, data(observables), observables)
# get_mean(FS2000, silent = true)
# get_SSS(FS2000, silent = true)
# # get_SSS(FS2000, algorithm = :third_order, silent = true)

# # import Plots, StatsPlots
# # plot_irf(FS2000)
# # plot_solution(FS2000,:k) # fix warning when there is no sensitivity and all values are the same. triggers: no strict ticks found...
# # plot_conditional_variance_decomposition(FS2000)
# end
# end
get_SS(FS2000)
get_SS(FS2000, parameters = :alp => 0.36)
get_solution(FS2000)
get_solution(FS2000, parameters = :alp => 0.35)
get_standard_deviation(FS2000)
get_correlation(FS2000)
get_autocorrelation(FS2000)
get_variance_decomposition(FS2000)
get_conditional_variance_decomposition(FS2000)
get_irf(FS2000)

data = simulate(FS2000)[:,:,1]
observables = [:c,:k]
calculate_kalman_filter_loglikelihood(FS2000, data(observables), observables)
get_mean(FS2000, silent = true)
get_SSS(FS2000, silent = true)
# get_SSS(FS2000, algorithm = :third_order, silent = true)

# import Plots, StatsPlots
# plot_irf(FS2000)
# plot_solution(FS2000,:k) # fix warning when there is no sensitivity and all values are the same. triggers: no strict ticks found...
# plot_conditional_variance_decomposition(FS2000)
end
end

end

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