alternative_rules_elasticities.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ff303fae-ce2b-41c5-8a4d-2793ee527b9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "using Compat, Random, Distributions, Plots, LinearAlgebra, Statistics, LaTeXStrings, SparseArrays, CPUTime, JLD2\n",
    "using SpecialFunctions, GSL, SuiteSparse, Distributed, Roots, NBInclude\n",
    "gr(fmt=:png)\n",
    "e = 2.7182818284\n",
    "Random.seed!(1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bd6d8537-88d1-44e0-9b48-32daefff9068",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3-element Vector{Int64}:\n",
       " 1\n",
       " 2\n",
       " 3"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "addprocs(2)\n",
    "@everywhere using Compat, Random, Distributions, Plots, LinearAlgebra, Statistics, LaTeXStrings, SparseArrays, CPUTime, JLD2, SpecialFunctions, GSL, SuiteSparse, Distributed\n",
    "@everywhere using SharedArrays, ParallelDataTransfer, Roots, NBInclude\n",
    "@everywhere @nbinclude(\"setup.ipynb\")\n",
    "@everywhere using .setup, .functions\n",
    "procs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a0a98387-96a2-4cd2-bcd6-50ab64ad7e29",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set the time grid\n",
    "T = 600\n",
    "dt = T/1200\n",
    "T_bar = 24\n",
    "time_grid = collect(0:dt:T)\n",
    "L = length(time_grid)\n",
    "t_grid = dt, L\n",
    "ψ = 0.02/12*exp.(-time_grid/12*1.15)\n",
    "\n",
    "# set up matrices that convert monthly responses to quarterly\n",
    "Lq = Int64((L-1)/8)\n",
    "A = Diagonal(ones(Lq))\n",
    "b = ones(8)'/8\n",
    "Q = hcat(kron(A,b),zeros(Lq));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "efe4b204-670d-4e03-9b2a-cb7d12bae69f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 40.752511 seconds (20.79 M allocations: 1.135 GiB, 0.93% gc time, 11.69% compilation time)\n"
     ]
    }
   ],
   "source": [
    "# compute steady-state statistics\n",
    "@load \"steady_state_output.jld2\"\n",
    "@load \"external_parameters.jld2\"\n",
    "par_grid_N, par_grid_T = deepcopy(par_grid), deepcopy(par_grid)\n",
    "par_grid_N.labor = par_grid.labor * prod_N\n",
    "par_grid_N.productivity = par_grid.productivity * prod_N\n",
    "par_grid_T.labor = par_grid.labor * prod_T\n",
    "par_grid_T.productivity = par_grid.productivity * prod_T\n",
    "aN = distr_N'*par_grid.assets\n",
    "aT = distr_T'*par_grid.assets\n",
    "a = par.ζ_T*aT + par.ζ_N*aN\n",
    "\n",
    "@time paral = pmap(q_response,[value_N,value_T],[distr_N,distr_T],(1-par.τ_w)*[ones(L),ones(L)],[par.r_d*ones(L),par.r_d*ones(L)],[zeros(L),zeros(L)],[true,true],[par,par],[par_grid_N,par_grid_T],[t_grid,t_grid])\n",
    " \n",
    "Cq_N, Lq_N, v_N = paral[1]\n",
    "Cq_T, Lq_T, v_T = paral[2]\n",
    "\n",
    "ss_objects = Cq_T, Cq_N, distr_T, distr_N;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c7e39d22-8ec2-4da1-b6e7-5124ca6062b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peg_low_elast  21.457239 seconds (25.92 k allocations: 2.214 MiB, 0.07% compilation time)\n",
      "change in variance = 1.59%: dispersion T = 0.05%, dispersion N = 0.36%, wealth bias T = 8.13%, wealth bias N = 36.33%, between = 55.12%\n",
      "P = -0.78 E = 0.0 r = 4.08 μ = 2.38, cN = -0.48 cT = -0.1 wN = -1.06 wT = -0.06\n",
      "low_075  20.643769 seconds (1.10 k allocations: 866.500 KiB)\n",
      "change in variance = -0.26%: dispersion T = -0.86%, dispersion N = -0.22%, wealth bias T = 168.53%, wealth bias N = 94.24%, between = -161.69%\n",
      "P = 1.38 E = 3.33 r = -0.72 μ = -2.42, cN = 0.7 cT = 0.88 wN = 0.88 wT = 1.39\n",
      "low_050  20.629009 seconds (1.11 k allocations: 867.922 KiB)\n",
      "change in variance = 0.18%: dispersion T = 0.63%, dispersion N = 0.02%, wealth bias T = -168.84%, wealth bias N = -30.36%, between = 298.56%\n",
      "P = 0.56 E = 1.81 r = 0.08 μ = -1.62, cN = 0.02 cT = 0.26 wN = 0.07 wT = 0.66\n",
      "low_025  20.899559 seconds (1.11 k allocations: 867.750 KiB)\n",
      "change in variance = 0.32%: dispersion T = 0.26%, dispersion N = 0.0%, wealth bias T = -78.82%, wealth bias N = 2.83%, between = 175.73%\n",
      "P = 0.28 E = 1.3 r = 0.34 μ = -1.36, cN = -0.2 cT = 0.05 wN = -0.19 wT = 0.42\n",
      "float_low_elast  20.216160 seconds (1.09 k allocations: 867.328 KiB)\n",
      "change in variance = 0.4%: dispersion T = 0.18%, dispersion N = 0.01%, wealth bias T = -58.62%, wealth bias N = 10.28%, between = 148.15%\n",
      "P = 0.15 E = 1.05 r = 0.47 μ = -1.23, cN = -0.31 cT = -0.05 wN = -0.33 wT = 0.3\n",
      "low_025_neg  20.336015 seconds (1.10 k allocations: 868.141 KiB)\n",
      "change in variance = 0.44%: dispersion T = 0.15%, dispersion N = 0.01%, wealth bias T = -49.7%, wealth bias N = 13.57%, between = 135.97%\n",
      "P = 0.07 E = 0.9 r = 0.55 μ = -1.15, cN = -0.38 cT = -0.11 wN = -0.41 wT = 0.23\n",
      "low_050_neg  21.249422 seconds (1.12 k allocations: 868.375 KiB)\n",
      "change in variance = 0.47%: dispersion T = 0.13%, dispersion N = 0.02%, wealth bias T = -44.67%, wealth bias N = 15.42%, between = 129.1%\n",
      "P = 0.01 E = 0.8 r = 0.61 μ = -1.09, cN = -0.42 cT = -0.15 wN = -0.46 wT = 0.18\n",
      "low_075_neg  20.455208 seconds (1.08 k allocations: 866.016 KiB)\n",
      "change in variance = 0.49%: dispersion T = 0.12%, dispersion N = 0.02%, wealth bias T = -41.45%, wealth bias N = 16.61%, between = 124.69%\n",
      "P = -0.03 E = 0.73 r = 0.65 μ = -1.06, cN = -0.46 cT = -0.18 wN = -0.5 wT = 0.15\n",
      "peg  20.260070 seconds (1.10 k allocations: 867.344 KiB)\n",
      "change in variance = 0.84%: dispersion T = 0.05%, dispersion N = 0.2%, wealth bias T = -4.27%, wealth bias N = 34.9%, between = 69.12%\n",
      "P = -0.5 E = 0.0 r = 3.3 μ = 1.6, cN = -0.52 cT = -0.27 wN = -0.71 wT = -0.02\n",
      "mix_075  20.372160 seconds (1.10 k allocations: 867.109 KiB)\n",
      "change in variance = -0.46%: dispersion T = -0.61%, dispersion N = -0.24%, wealth bias T = 104.33%, wealth bias N = 70.59%, between = -74.07%\n",
      "P = 1.44 E = 3.26 r = -0.53 μ = -2.23, cN = 0.68 cT = 0.83 wN = 1.02 wT = 1.43\n",
      "mix_050  20.570620 seconds (1.11 k allocations: 867.812 KiB)\n",
      "change in variance = -0.09%: dispersion T = -1.73%, dispersion N = -0.31%, wealth bias T = 383.49%, wealth bias N = 164.52%, between = -445.97%\n",
      "P = 0.62 E = 1.71 r = 0.27 μ = -1.44, cN = 0.02 cT = 0.2 wN = 0.24 wT = 0.7\n",
      "mix_025  25.852527 seconds (1.25 k allocations: 870.891 KiB)\n",
      "change in variance = 0.03%: dispersion T = 3.8%, dispersion N = 0.47%, wealth bias T = -918.35%, wealth bias N = -273.61%, between = 1287.69%\n",
      "P = 0.34 E = 1.2 r = 0.53 μ = -1.17, cN = -0.2 cT = -0.01 wN = -0.03 wT = 0.46\n",
      "float  20.608290 seconds (1.11 k allocations: 866.453 KiB)\n",
      "change in variance = 0.1%: dispersion T = 1.17%, dispersion N = 0.12%, wealth bias T = -295.73%, wealth bias N = -64.01%, between = 458.45%\n",
      "P = 0.21 E = 0.95 r = 0.67 μ = -1.03, cN = -0.31 cT = -0.11 wN = -0.16 wT = 0.33\n",
      "mix_025_neg  20.006980 seconds (1.09 k allocations: 867.250 KiB)\n",
      "change in variance = 0.13%: dispersion T = 0.78%, dispersion N = 0.08%, wealth bias T = -202.42%, wealth bias N = -32.6%, between = 334.17%\n",
      "P = 0.13 E = 0.8 r = 0.75 μ = -0.95, cN = -0.37 cT = -0.18 wN = -0.23 wT = 0.26\n",
      "mix_050_neg  19.982733 seconds (1.08 k allocations: 866.859 KiB)\n",
      "change in variance = 0.16%: dispersion T = 0.63%, dispersion N = 0.06%, wealth bias T = -164.79%, wealth bias N = -19.94%, between = 284.04%\n",
      "P = 0.07 E = 0.69 r = 0.8 μ = -0.9, cN = -0.42 cT = -0.22 wN = -0.29 wT = 0.21\n",
      "mix_075_neg  20.027104 seconds (1.08 k allocations: 867.078 KiB)\n",
      "change in variance = 0.18%: dispersion T = 0.54%, dispersion N = 0.05%, wealth bias T = -144.45%, wealth bias N = -13.1%, between = 256.95%\n",
      "P = 0.03 E = 0.62 r = 0.84 μ = -0.86, cN = -0.45 cT = -0.25 wN = -0.32 wT = 0.18\n",
      "peg_high_elast  20.223523 seconds (1.09 k allocations: 867.172 KiB)\n",
      "change in variance = 0.29%: dispersion T = 0.21%, dispersion N = 0.15%, wealth bias T = -51.52%, wealth bias N = 24.86%, between = 126.3%\n",
      "P = -0.29 E = 0.0 r = 2.7 μ = 1.0, cN = -0.57 cT = -0.41 wN = -0.44 wT = -0.01\n",
      "high_075  20.771684 seconds (1.11 k allocations: 868.406 KiB)\n",
      "change in variance = -0.67%: dispersion T = -0.5%, dispersion N = -0.28%, wealth bias T = 77.57%, wealth bias N = 61.17%, between = -37.97%\n",
      "P = 1.5 E = 3.21 r = -0.35 μ = -2.05, cN = 0.68 cT = 0.78 wN = 1.17 wT = 1.46\n",
      "high_050  21.247078 seconds (1.12 k allocations: 867.859 KiB)\n",
      "change in variance = -0.33%: dispersion T = -0.6%, dispersion N = -0.24%, wealth bias T = 116.16%, wealth bias N = 72.09%, between = -87.42%\n",
      "P = 0.68 E = 1.65 r = 0.46 μ = -1.24, cN = 0.02 cT = 0.14 wN = 0.4 wT = 0.72\n",
      "high_025  20.601235 seconds (1.11 k allocations: 867.734 KiB)\n",
      "change in variance = -0.22%: dispersion T = -0.75%, dispersion N = -0.26%, wealth bias T = 155.53%, wealth bias N = 83.23%, between = -137.75%\n",
      "P = 0.4 E = 1.13 r = 0.73 μ = -0.97, cN = -0.2 cT = -0.07 wN = 0.14 wT = 0.47\n",
      "float_high_elast  20.198397 seconds (1.11 k allocations: 867.891 KiB)\n",
      "change in variance = -0.17%: dispersion T = -0.91%, dispersion N = -0.3%, wealth bias T = 195.48%, wealth bias N = 94.54%, between = -188.81%\n",
      "P = 0.27 E = 0.87 r = 0.86 μ = -0.84, cN = -0.31 cT = -0.17 wN = 0.02 wT = 0.35\n",
      "high_025_neg  20.555591 seconds (1.11 k allocations: 867.703 KiB)\n",
      "change in variance = -0.13%: dispersion T = -1.08%, dispersion N = -0.35%, wealth bias T = 236.01%, wealth bias N = 106.02%, between = -240.61%\n",
      "P = 0.19 E = 0.72 r = 0.94 μ = -0.76, cN = -0.37 cT = -0.24 wN = -0.06 wT = 0.28\n",
      "high_050_neg  20.212186 seconds (1.09 k allocations: 866.094 KiB)\n",
      "change in variance = -0.11%: dispersion T = -1.25%, dispersion N = -0.39%, wealth bias T = 277.11%, wealth bias N = 117.68%, between = -293.14%\n",
      "P = 0.13 E = 0.62 r = 1.0 μ = -0.7, cN = -0.42 cT = -0.28 wN = -0.11 wT = 0.23\n",
      "high_075_neg  20.253252 seconds (1.09 k allocations: 867.625 KiB)\n",
      "change in variance = -0.09%: dispersion T = -1.42%, dispersion N = -0.44%, wealth bias T = 318.77%, wealth bias N = 129.49%, between = -346.39%\n",
      "P = 0.1 E = 0.54 r = 1.04 μ = -0.66, cN = -0.45 cT = -0.31 wN = -0.15 wT = 0.19\n"
     ]
    }
   ],
   "source": [
    "Δvars, biases_T, biases_N, P_arr, E_arr, r_arr, μ_arr, cN_arr, cT_arr, wN_arr, wT_arr = [], [], [], [], [], [], [], [], [], [], []\n",
    "var_total = 0.0\n",
    "\n",
    "ress = [\"peg_low_elast\", \"low_075\", \"low_050\", \"low_025\", \"float_low_elast\", \"low_025_neg\", \"low_050_neg\", \"low_075_neg\", \n",
    "    \"peg\", \"mix_075\", \"mix_050\", \"mix_025\", \"float\", \"mix_025_neg\", \"mix_050_neg\", \"mix_075_neg\", \n",
    "    \"peg_high_elast\", \"high_075\", \"high_050\", \"high_025\", \"float_high_elast\", \"high_025_neg\", \"high_050_neg\", \"high_075_neg\"]\n",
    "\n",
    "for i in 1:length(ress)\n",
    "    @load \"transition_results/\"*ress[i]*\".jld2\" res_this\n",
    "    \n",
    "    print(ress[i],\" \")\n",
    "\n",
    "    @time paral = pmap(q_response,[value_N,value_T],[distr_N,distr_T],(1-par.τ_w)*[res_this.wN_sequence,res_this.wT_sequence],[res_this.rd_sequence,res_this.rd_sequence],[zeros(L),zeros(L)],[true,true],[par,par],[par_grid_N,par_grid_T],[t_grid,t_grid])\n",
    "    Call_N, Lall_N, Wall_N = paral[1]\n",
    "    Call_T, Lall_T, Wall_T = paral[2]\n",
    "    \n",
    "    var_Q_total, var_total, change_var, disp_T, disp_N, bias_T, bias_N, betw = print_decomposition(Call_T,Call_N,par,par_grid,ss_objects)\n",
    "    append!(Δvars,change_var)\n",
    "    append!(biases_T,bias_T)\n",
    "    append!(biases_N,bias_N)\n",
    "    \n",
    "    P_seq = exp.(cumsum(vcat(0,res_this.π_sequence[1:end-1]) * dt))\n",
    "    E_seq = P_seq.*res_this.pF_sequence\n",
    "    \n",
    "    append!(P_arr,100*(Q*P_seq)[1]-100)\n",
    "    append!(E_arr,100*(Q*E_seq)[1]-100)\n",
    "    append!(r_arr,100*12*((Q*res_this.rd_sequence)[1]-par.r_d))\n",
    "    append!(μ_arr,100*12*(Q*res_this.μ_sequence)[1])\n",
    "    append!(cN_arr,100*(Q*res_this.cN_sequence)[1]/res_this.cN_sequence[end]-100)\n",
    "    append!(cT_arr,100*(Q*res_this.cT_sequence)[1]/res_this.cT_sequence[end]-100)\n",
    "    append!(wN_arr,100*(Q*res_this.wN_sequence)[1]-100)\n",
    "    append!(wT_arr,100*(Q*res_this.wT_sequence)[1]-100)\n",
    "    \n",
    "    print(\"\\nP = \",round(P_arr[end],digits=2),\" E = \",round(E_arr[end],digits=2),\" r = \",round(r_arr[end],digits=2),\" μ = \",round(μ_arr[end],digits=2))\n",
    "    print(\", cN = \",round(cN_arr[end],digits=2),\" cT = \",round(cT_arr[end],digits=2),\" wN = \",round(wN_arr[end],digits=2),\" wT = \",round(wT_arr[end],digits=2),\"\\n\")\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "15a1da6d-1ed7-45eb-acfb-f573a84b9b1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "ϕϵ_arr = [-Inf, 0.75, 0.5, 0.25, 0, -0.25, -0.5, -0.75]\n",
    "ϕϵ_arr = repeat(ϕϵ_arr,3)\n",
    "ϕπ_arr = 1.5*(ones(length(ϕϵ_arr))-ϕϵ_arr)\n",
    "price_level_arr, inf_arr, psi_arr, logE_arr, π_arr, initial_depr, eventual_depr = [], [], [], [], [], [], []\n",
    "\n",
    "for i in 1:length(ress)\n",
    "    @load \"transition_results/\"*ress[i]*\".jld2\" res_this\n",
    "    \n",
    "    P_seq = exp.(cumsum(vcat(0,res_this.π_sequence[1:end-1]) * dt))\n",
    "    E_seq = P_seq.*res_this.pF_sequence\n",
    "    append!(logE_arr,(Q*log.(E_seq))[1])\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "99deb494-0da9-4666-a09b-bb1a7f4ff2fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "degrees_of_fear = reverse([L\"0.75\",L\"0.5\",L\"0.25\",L\"0\",L\"-0.25\",L\"-0.5\",L\"-0.75\"])\n",
    "\n",
    "Δgap = Δvars - biases_T - biases_N\n",
    "plot_w = plot(1:7,reverse(wT_arr[2:8]),xticks=(1:7,degrees_of_fear),color=:black,label=L\"\\mathrm{T}\",title=L\"\\mathrm{(a)\\ wage\\ response}\",xlabel=L\"\\phi_\\varepsilon\",lw=2,legendfontsize=7)\n",
    "plot!(1:7,reverse(wN_arr[18:end]),xticks=(1:7,degrees_of_fear),color=:beige,ms=5,markershape=:circle,label=L\"\\mathrm{N,\\ high\\ }\\{\\theta\\}\")\n",
    "plot!(1:7,reverse(wN_arr[10:16]),xticks=(1:7,degrees_of_fear),color=:beige,ms=5,markershape=:diamond,label=L\"\\mathrm{N,\\ baseline}\")\n",
    "plot!(1:7,reverse(wN_arr[2:8]),xticks=(1:7,degrees_of_fear),color=:beige,ms=5,markershape=:rect,label=L\"\\mathrm{N,\\ low\\ }\\{\\theta\\}\",legend=:topleft)\n",
    "plot_C = plot(1:7,reverse(cT_arr[2:8]),xticks=(1:7,degrees_of_fear),color=:sienna,markershape=:rect,ms=5,label=L\"\\mathrm{T,\\ low\\ }\\{\\theta\\}\",title=L\"\\mathrm{(b)\\ consumption\\ response}\",xlabel=L\"\\phi_\\varepsilon\",legendfontsize=7)\n",
    "plot!(1:7,reverse(cT_arr[10:16]),xticks=(1:7,degrees_of_fear),color=:sienna,ms=5,markershape=:diamond,label=L\"\\mathrm{T,\\ baseline}\")\n",
    "plot!(1:7,reverse(cT_arr[18:end]),xticks=(1:7,degrees_of_fear),color=:sienna,ms=5,markershape=:circle,label=L\"\\mathrm{T,\\ high\\ }\\{\\theta\\}\")\n",
    "plot!(1:7,reverse(cN_arr[2:8]),xticks=(1:7,degrees_of_fear),color=:black,label=L\"\\mathrm{N}\",legend=:topleft,lw=2)\n",
    "\n",
    "matr_biases_N = reshape(biases_N,8,3)[2:end,:]\n",
    "matr_biases_T = reshape(biases_T,8,3)[2:end,:]\n",
    "matr_r = reshape(r_arr,8,3)[2:end,:]\n",
    "matr_C = reshape(cT_arr-cN_arr,8,3)[2:end,:]\n",
    "matr_w = reshape(wT_arr-wN_arr,8,3)[2:end,:]\n",
    "matr_E = 100*reshape(logE_arr,8,3)[2:end,:]\n",
    "matr_gaps = reshape(Δgap,8,3)[2:end,:]\n",
    "\n",
    "clims = extrema(100*[matr_biases_T;matr_biases_N;matr_gaps]/var_total)\n",
    "degrees_of_fear = reverse([L\"0.75\",L\"0.50\",L\"0.25\",L\"\\phi_\\varepsilon\\ \\ 0\",L\"-0.25\",L\"-0.50\",L\"-0.75\"])\n",
    "plot_N_bias = heatmap(100*matr_biases_N/var_total,c=cgrad(:ice, rev=true),xticks=(1:3,[L\"\\mathrm{low}\",L\"\\mathrm{baseline}\",L\"\\mathrm{high}\"]),yticks=(1:7,reverse(degrees_of_fear)),title=L\"\\mathrm{(d)\\ wealth\\ bias\\ N}\",colorbar=:false,clims=clims,xlabel=L\"\\{\\theta\\}\")\n",
    "plot_T_bias = heatmap(100*matr_biases_T/var_total,c=cgrad(:ice, rev=true),title=L\"\\mathrm{(e)\\ wealth\\ bias\\ T}\",xticks=(1:3,[L\"\\mathrm{low}\",L\"\\mathrm{baseline}\",L\"\\mathrm{high}\"]),yticks=(1:7,reverse(degrees_of_fear)),colorbar=:false,clims=clims,xlabel=L\"\\{\\theta\\}\")\n",
    "plot_gaps = heatmap(100*matr_gaps/var_total,c=cgrad(:ice, rev=true),title=L\"\\mathrm{(f)\\ change\\ in\\ gap}\",xticks=(1:3,[L\"\\mathrm{low}\",L\"\\mathrm{baseline}\",L\"\\mathrm{high}\"]),yticks=(1:7,reverse(degrees_of_fear)),colorbar=:true,clims=clims,xlabel=L\"\\{\\theta\\}\")\n",
    "plot_r = heatmap(matr_r,c=:algae,title=L\"\\mathrm{(c)\\ real\\ interest\\ rate}\",xticks=(1:3,[L\"\\mathrm{low}\",L\"\\mathrm{baseline}\",L\"\\mathrm{high}\"]),yticks=(1:7,reverse(degrees_of_fear)),colorbar=:true,xlabel=L\"\\{\\theta\\}\")\n",
    "plot_E = heatmap(matr_E,c=:amp,title=L\"\\mathrm{nominal\\ exchange\\ rate}\",xticks=(1:3,[L\"\\mathrm{low}\",L\"\\mathrm{baseline}\",L\"\\mathrm{high}\"]),yticks=(1:7,reverse(degrees_of_fear)),colorbar=:true,xlabel=L\"\\{\\theta\\}\")\n",
    "l = @layout[a a a{0.4w}; a a a{0.4w}]\n",
    "plot_all = plot(plot_w,plot_C,plot_r,plot_N_bias,plot_T_bias,plot_gaps,size=(900,600),layout=l,tickfontsize=10,titlefontsize=12,bottom_margin=0.25Plots.cm,top_margin=0.25Plots.cm,left_margin=-0.2Plots.cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "59e40d37-f968-4519-abbe-3b7e4113bc59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"graphs/alternative_rules_elasticites.png\""
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "png(\"graphs/alternative_rules_elasticites.png\")"
   ]
  }
 ],
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