diff --git a/examples/notebooks/rbc_model.ipynb b/examples/notebooks/rbc_model.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Solving the RBC model"
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "source": [
+ "using Dolo"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "\n",
+ "The RBC model is defined in a YAML file which we can read locally."
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "source": [
+ "filename= \"../models/rbc.yaml\"\n",
+ "\n",
+ "readlines(filename)"
+ ],
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "87-element Vector{String}:\n",
+ " \"name: Real Business Cycle\"\n",
+ " \"\"\n",
+ " \"model_type: dtcc\"\n",
+ " \"\"\n",
+ " \"symbols:\"\n",
+ " \"\"\n",
+ " \" exogenous: [z]\"\n",
+ " \" states: [k]\"\n",
+ " \" controls: [n, i]\"\n",
+ " \" expectations: [m]\"\n",
+ " ⋮\n",
+ " \"\"\n",
+ " \"options:\"\n",
+ " \" grid: !Cartesian\"\n",
+ " \" orders: [50]\"\n",
+ " \"\"\n",
+ " \"test: |\"\n",
+ " \" chi*n^eta*c^sigma - (1-alpha)*y/n | 0 <= n <= inf\"\n",
+ " \" 1 - beta*(c/c(1))^(sigma)*(1-delta+rk(1)) | 0 <= i <= inf\"\n",
+ " \" \""
+ ]
+ },
+ "metadata": {}
+ }
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "\n",
+ "yaml_import(filename) reads the YAML file and generates a model object."
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "source": [
+ "model = yaml_import(filename)"
+ ],
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "[0.000256]\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Model"
+ ]
+ },
+ "metadata": {}
+ }
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "The model file already has values for steady-state variables stated in the calibration section so we can go ahead and check that they are correct by computing the model equations at the steady state."
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "source": [
+ "residuals(model)"
+ ],
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Dict{Symbol, Vector{Float64}} with 2 entries:\n",
+ " :transition => [0.0]\n",
+ " :arbitrage => [-4.44089e-16, 0.0]"
+ ]
+ },
+ "metadata": {}
+ }
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Printing the model also lets us have a look at all the model equations and check that all residual errors are 0 at the steady-state, but with less display prescision."
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "source": [
+ "Base.show(model)"
+ ],
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Model"
+ ]
+ }
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Next we compute a solution to the model using a first order perturbation method (see the source for the approximate_controls function). The result is a decsion rule object. By decision rule we refer to any object that is callable and maps states to decisions. This particular decision rule object is a TaylorExpansion (see the source for the TaylorExpansion class)."
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "source": [
+ "dr_pert = perturb(model)"
+ ],
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Perturbation Results\n",
+ " * Decision Rule type: Dolo.BiTaylorExpansion{2}\n",
+ " * stable < true\n",
+ " * determined < true\n"
+ ]
+ },
+ "metadata": {}
+ }
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "\n",
+ "We now compute the global solution (see the source for the time_iteration function). It returns a decision rule object of type SmolyakGrid (see the source for the SmolyakGrid class)."
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "source": [
+ "dr_global = time_iteration(model)"
+ ],
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "------------------------------------------------------------------\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "It ϵₙ ηₙ=|xₙ-xₙ₋₁| λₙ=ηₙ/ηₙ₋₁ Time Newton steps\n",
+ "------------------------------------------------------------------\n",
+ "1 2.18e+00 6.18e-01 NaN 1.59e+09 9 \n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "2 2.01e-01 5.31e-02 8.59e-02 2.01e+07 6 \n",
+ "3 1.24e-01 4.22e-02 7.95e-01 2.26e+07 6 \n",
+ "4 8.16e-02 3.36e-02 7.98e-01 3.86e+07 6 \n",
+ "5 5.87e-02 2.73e-02 8.12e-01 1.68e+07 5 \n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "6 4.68e-02 2.26e-02 8.27e-01 1.80e+07 5 \n",
+ "7 3.85e-02 1.96e-02 8.68e-01 1.99e+07 5 \n",
+ "8 3.25e-02 1.72e-02 8.77e-01 1.63e+07 5 \n",
+ "9 2.81e-02 1.53e-02 8.92e-01 1.68e+07 5 \n",
+ "10 2.45e-02 1.37e-02 8.96e-01 2.03e+07 5 \n",
+ "11 2.16e-02 1.24e-02 9.01e-01 1.95e+07 4 \n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "12 1.86e-02 1.09e-02 8.81e-01 1.69e+07 4 \n",
+ "13 1.61e-02 9.62e-03 8.83e-01 1.50e+07 4 \n",
+ "14 1.42e-02 8.63e-03 8.97e-01 1.53e+07 4 \n",
+ "15 1.27e-02 7.80e-03 9.04e-01 3.49e+07 4 \n",
+ "16 1.13e-02 7.03e-03 9.01e-01 1.37e+07 4 \n",
+ "17 1.01e-02 6.32e-03 8.99e-01 1.35e+07 4 \n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "18 9.04e-03 5.69e-03 9.01e-01 1.35e+07 4 \n",
+ "19 8.12e-03 5.15e-03 9.04e-01 1.34e+07 4 \n",
+ "20 7.32e-03 4.67e-03 9.07e-01 1.34e+07 4 \n",
+ "21 6.62e-03 4.25e-03 9.09e-01 1.40e+07 4 \n",
+ "22 6.01e-03 3.87e-03 9.11e-01 1.37e+07 4 \n",
+ "23 5.46e-03 3.53e-03 9.13e-01 1.44e+07 4 \n",
+ "24 4.97e-03 3.23e-03 9.14e-01 1.56e+07 4 \n",
+ "25 4.54e-03 2.96e-03 9.16e-01 1.50e+07 4 \n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "26 4.15e-03 2.71e-03 9.17e-01 1.88e+07 4 \n",
+ "27 3.80e-03 2.49e-03 9.18e-01 1.42e+07 4 \n",
+ "28 3.48e-03 2.28e-03 9.18e-01 3.11e+07 3 \n",
+ "29 3.19e-03 2.10e-03 9.19e-01 1.10e+07 3 \n",
+ "30 2.93e-03 1.93e-03 9.20e-01 1.13e+07 3 \n",
+ "31 2.69e-03 1.78e-03 9.20e-01 1.12e+07 3 \n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "32 2.47e-03 1.64e-03 9.21e-01 1.09e+07 3 \n",
+ "33 2.27e-03 1.51e-03 9.21e-01 1.13e+07 3 \n",
+ "34 2.09e-03 1.39e-03 9.21e-01 1.13e+07 3 \n",
+ "35 1.92e-03 1.28e-03 9.22e-01 1.04e+07 3 \n",
+ "36 1.77e-03 1.18e-03 9.22e-01 1.16e+07 3 \n",
+ "37 1.63e-03 1.09e-03 9.22e-01 1.09e+07 3 \n",
+ "38 1.50e-03 1.00e-03 9.22e-01 9.97e+06 3 \n",
+ "39 1.38e-03 9.25e-04 9.22e-01 1.09e+07 3 \n",
+ "40 1.28e-03 8.53e-04 9.22e-01 1.11e+07 3 \n",
+ "41 1.18e-03 7.87e-04 9.22e-01 1.00e+07 3 \n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "42 1.08e-03 7.26e-04 9.22e-01 1.12e+07 3 \n",
+ "43 9.99e-04 6.70e-04 9.22e-01 1.09e+07 3 \n",
+ "44 9.21e-04 6.18e-04 9.22e-01 9.57e+06 3 \n",
+ "45 8.49e-04 5.70e-04 9.22e-01 2.72e+07 3 \n",
+ "46 7.82e-04 5.26e-04 9.22e-01 9.72e+06 3 \n",
+ "47 7.21e-04 4.85e-04 9.22e-01 1.13e+07 3 \n",
+ "48 6.64e-04 4.47e-04 9.22e-01 1.11e+07 3 \n",
+ "49 6.12e-04 4.12e-04 9.22e-01 1.00e+07 3 \n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "50 5.64e-04 3.80e-04 9.22e-01 1.14e+07 3 \n",
+ "51 5.20e-04 3.50e-04 9.22e-01 1.01e+07 3 \n",
+ "52 4.79e-04 3.22e-04 9.21e-01 1.06e+07 3 \n",
+ "53 4.41e-04 2.97e-04 9.21e-01 1.07e+07 3 \n",
+ "54 4.06e-04 2.73e-04 9.21e-01 1.10e+07 3 \n",
+ "55 3.74e-04 2.52e-04 9.21e-01 1.07e+07 3 \n",
+ "56 3.44e-04 2.32e-04 9.21e-01 1.14e+07 3 \n",
+ "57 3.16e-04 2.13e-04 9.20e-01 1.05e+07 3 \n",
+ "58 2.91e-04 1.96e-04 9.20e-01 1.04e+07 3 \n",
+ "59 2.68e-04 1.81e-04 9.20e-01 1.15e+07 3 \n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "60 2.46e-04 1.66e-04 9.19e-01 1.00e+07 3 \n",
+ "61 2.26e-04 1.53e-04 9.19e-01 1.06e+07 3 \n",
+ "62 2.08e-04 1.40e-04 9.19e-01 2.95e+07 3 \n",
+ "63 1.91e-04 1.29e-04 9.19e-01 1.23e+07 3 \n",
+ "64 1.75e-04 1.18e-04 9.18e-01 1.20e+07 3 \n",
+ "65 1.61e-04 1.09e-04 9.18e-01 1.11e+07 3 \n",
+ "66 1.47e-04 9.96e-05 9.17e-01 1.07e+07 3 \n",
+ "67 1.35e-04 9.13e-05 9.17e-01 1.00e+07 3 \n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "68 1.24e-04 8.37e-05 9.17e-01 1.15e+07 3 \n",
+ "69 1.14e-04 7.67e-05 9.16e-01 1.21e+07 3 \n",
+ "70 1.04e-04 7.03e-05 9.16e-01 9.92e+06 3 \n",
+ "71 9.52e-05 6.43e-05 9.15e-01 1.14e+07 3 \n",
+ "72 8.71e-05 5.88e-05 9.15e-01 1.17e+07 3 \n",
+ "73 7.96e-05 5.38e-05 9.14e-01 9.83e+06 3 \n",
+ "74 7.28e-05 4.92e-05 9.14e-01 1.12e+07 3 \n",
+ "75 6.65e-05 4.49e-05 9.13e-01 1.08e+07 3 \n",
+ "76 6.07e-05 4.10e-05 9.13e-01 1.50e+07 3 \n",
+ "77 5.53e-05 3.74e-05 9.12e-01 9.94e+06 3 \n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "78 5.04e-05 3.41e-05 9.12e-01 1.11e+07 3 \n",
+ "79 4.60e-05 3.11e-05 9.11e-01 2.22e+07 2 \n",
+ "80 4.18e-05 2.83e-05 9.10e-01 8.33e+06 2 \n",
+ "81 3.80e-05 2.57e-05 9.09e-01 7.29e+06 2 \n",
+ "82 3.46e-05 2.34e-05 9.09e-01 7.81e+06 2 \n",
+ "83 3.14e-05 2.12e-05 9.08e-01 7.15e+06 2 \n",
+ "84 2.85e-05 1.92e-05 9.07e-01 7.50e+06 2 \n",
+ "85 2.58e-05 1.74e-05 9.06e-01 8.41e+06 2 \n",
+ "86 2.33e-05 1.58e-05 9.05e-01 7.26e+06 2 \n",
+ "87 2.11e-05 1.43e-05 9.04e-01 7.44e+06 2 \n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "88 1.90e-05 1.29e-05 9.03e-01 6.70e+06 2 \n",
+ "89 1.72e-05 1.16e-05 9.02e-01 8.30e+06 2 \n",
+ "90 1.55e-05 1.04e-05 9.00e-01 7.05e+06 2 \n",
+ "91 1.39e-05 9.39e-06 8.99e-01 8.04e+06 2 \n",
+ "92 1.25e-05 8.43e-06 8.97e-01 7.04e+06 2 \n",
+ "93 1.12e-05 7.54e-06 8.95e-01 7.95e+06 2 \n",
+ "94 9.97e-06 6.74e-06 8.94e-01 7.01e+06 2 \n",
+ "95 8.89e-06 6.01e-06 8.91e-01 7.73e+06 2 \n",
+ "96 7.90e-06 5.34e-06 8.89e-01 7.11e+06 2 \n",
+ "97 7.01e-06 4.74e-06 8.87e-01 7.29e+06 2 \n",
+ "98 6.19e-06 4.19e-06 8.84e-01 7.15e+06 2 \n",
+ "99 5.45e-06 3.69e-06 8.81e-01 7.04e+06 2 \n",
+ "100 4.78e-06 3.23e-06 8.77e-01 8.23e+06 2 \n",
+ "101 4.17e-06 2.82e-06 8.73e-01 6.60e+06 2 \n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "102 3.62e-06 2.45e-06 8.68e-01 7.81e+06 2 \n",
+ "103 3.13e-06 2.11e-06 8.63e-01 2.26e+07 2 \n",
+ "104 2.68e-06 1.81e-06 8.56e-01 7.61e+06 2 \n",
+ "105 2.27e-06 1.54e-06 8.49e-01 6.82e+06 2 \n",
+ "106 1.91e-06 1.29e-06 8.39e-01 7.03e+06 2 \n",
+ "107 1.58e-06 1.07e-06 8.28e-01 8.10e+06 2 \n",
+ "108 1.28e-06 8.67e-07 8.13e-01 6.54e+06 2 \n",
+ "109 1.02e-06 6.89e-07 7.94e-01 8.06e+06 2 \n",
+ "110 7.82e-07 5.29e-07 7.68e-01 6.75e+06 2 \n",
+ "111 5.71e-07 3.86e-07 7.30e-01 7.74e+06 2 \n",
+ "112 3.84e-07 2.59e-07 6.71e-01 7.08e+06 2 \n",
+ "113 2.17e-07 1.47e-07 5.65e-01 7.60e+06 2 \n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "114 6.92e-08 4.68e-08 3.19e-01 7.27e+06 2 \n",
+ "------------------------------------------------------------------\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Results of Time Iteration Algorithm\n",
+ " * Complementarities: true\n",
+ " * Discretized Process type: Dolo.DiscretizedProcess{Dolo.CartesianGrid{1}}\n",
+ " * Decision Rule type: Dolo.CubicDR{Dolo.CartesianGrid{1}, Dolo.CartesianGrid{1}, 2, 2}\n",
+ " * Number of iterations: 114\n",
+ " * Convergence: true\n",
+ " * |x - x'| < 1.0e-07: true\n"
+ ]
+ },
+ "metadata": {}
+ }
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Decision rule\n",
+ "Here we plot optimal investment and labour for different levels of capital (see the source for the plot_decision_rule function)."
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "source": [
+ "tab_global = tabulate(model, dr_global.dr, :k)\n",
+ "tab_pert = tabulate(model, dr_pert.dr, :k)"
+ ],
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "2-dimensional AxisArray{Float64,2,...} with axes:\n",
+ " :V, [:z, :k, :n, :i, :y, :c, :rk, :w]\n",
+ " :k, [4.677489145072993, 4.771983875276488, 4.8664786054799825, 4.9609733356834775, 5.055468065886973, 5.149962796090467, 5.244457526293962, 5.338952256497457, 5.433446986700952, 5.527941716904446 … 13.182014863387526, 13.27650959359102, 13.371004323794516, 13.46549905399801, 13.559993784201506, 13.654488514405001, 13.748983244608494, 13.84347797481199, 13.937972705015484, 14.03246743521898]\n",
+ "And data, a 8×100 adjoint(::Matrix{Float64}) with eltype Float64:\n",
+ " 0.0 0.0 0.0 … 0.0 0.0 0.0\n",
+ " 4.67749 4.77198 4.86648 13.8435 13.938 14.0325\n",
+ " 0.401476 0.400032 0.398589 0.261411 0.259968 0.258524\n",
+ " 0.283066 0.282072 0.281078 0.186671 0.185677 0.184683\n",
+ " 0.902727 0.906514 0.910191 0.968765 0.967346 0.965894\n",
+ " 0.619661 0.624442 0.629113 … 0.782094 0.781669 0.78121\n",
+ " 0.063688 0.0626887 0.0617208 0.0230934 0.0229032 0.0227148\n",
+ " 1.50651 1.51829 1.52997 2.48295 2.49309 2.50325"
+ ]
+ },
+ "metadata": {}
+ }
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "source": [
+ "using Plots\n",
+ "\n",
+ "p1 = plot(tab_global[V=:k],tab_global[V=:i],label = \"Global\", title = \"Investment\", xlabel = \"k\", ylabel = \"i\")\n",
+ "plot!(p1,tab_pert[V=:k],tab_pert[V=:i],label = \"Perturbation\")\n",
+ "\n",
+ "p2 = plot(tab_global[V=:k],tab_global[V=:n],label = \"Global\", title = \"Labour\", xlabel = \"k\", ylabel = \"n\")\n",
+ "plot!(p2,tab_pert[V=:k],tab_pert[V=:n],label = \"Perturbation\")\n",
+ "\n",
+ "plot(p1,p2,layout = (1,2))"
+ ],
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/html": [
+ "\n",
+ "\n"
+ ],
+ "image/png": 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",
+ "image/svg+xml": "\n\n"
+ },
+ "metadata": {}
+ }
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "\n",
+ "It would seem, according to this, that second order perturbation does very well for the RBC model. We will revisit this issue more rigorously when we explore the deviations from the model's arbitrage section equations.\n",
+ "Let us repeat the calculation of investment decisions for various values of the depreciation rate, $\\delta$. Note that this is a comparative statics exercise, even though the models compared are dynamic."
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "source": [
+ "original_delta = model.calibration.flat[:delta] \n",
+ "\n",
+ "drs = []\n",
+ "delta_values = LinRange(0.01, 0.04,5)\n",
+ "for val in delta_values\n",
+ " set_calibration!(model,:delta,val)\n",
+ " push!(drs,perturb(model).dr)\n",
+ "end\n",
+ "\n",
+ "p = plot()\n",
+ "for (j,dr) in enumerate(drs)\n",
+ " sim = tabulate(model, dr,:k)\n",
+ " plot!(p,sim[:k],sim[:i],label=\"δ= $(delta_values[j])\", ylabel=\"i\", title = \"Investment\")\n",
+ "end\n",
+ "\n",
+ "set_calibration!(model,:delta,original_delta)\n",
+ "\n",
+ "p"
+ ],
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "[0.000256]\n",
+ "[0.000256]\n",
+ "[0.000256]\n",
+ "[0.000256]\n",
+ "[0.000256]\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "[0.000256]\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/html": [
+ "\n",
+ "\n"
+ ],
+ "image/png": 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",
+ "image/svg+xml": "\n\n"
+ },
+ "metadata": {}
+ }
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "\n",
+ "We find that more durable capital leads to higher steady state investment and slows the rate of convergence for capital (the slopes are roughly the same, which implies that relative to steady state capital investment responds stronger at higher $\\delta$; this is in addition to the direct effect of depreciation).\n",
+ "# Use the model to simulate\n",
+ "We will use the deterministic steady-state as a starting point."
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "source": [
+ "s0 = model.calibration[:states]\n",
+ "print(\"$(model.symbols[:states]) = $(s0)\")"
+ ],
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "[:k] = [9.354978290145986]"
+ ]
+ }
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "We also get the covariance matrix just in case. This is a one shock model so all we have is the variance of $e_z$."
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "source": [
+ "sigma2_ez = model.exogenous.Sigma\n",
+ "sigma2_ez"
+ ],
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "1×1 Matrix{Float64}:\n",
+ " 0.000256"
+ ]
+ },
+ "metadata": {}
+ }
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Impulse response functions\n",
+ "Consider a 10% shock to productivity."
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "source": [
+ "s1 = copy(s0)\n",
+ "print(\"$(model.symbols[:states]) = $(s1)\")"
+ ],
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "[:k] = [9.354978290145986]"
+ ]
+ }
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "The simulate function is used both to trace impulse response functions and to compute stochastic simulations. Choosing n_exp>=1, will result in that many \"stochastic\" simulations. With n_exp = 0, we get one single simulation without any stochastic shock (see the source for the simulate function). The output is a panda table of size $H \\times n_v$ where $n_v$ is the number of variables in the model and $H$ the number of dates."
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "source": [
+ "dr = dr_pert.dr\n",
+ "simulate(model, dr, N=10, T=40)"
+ ],
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "3-dimensional AxisArray{Float64,3,...} with axes:\n",
+ " :N, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
+ " :V, [:z, :k, :n, :i, :y, :c, :rk, :w]\n",
+ " :T, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10 … 31, 32, 33, 34, 35, 36, 37, 38, 39, 40]\n",
+ "And data, a 10×8×40 Array{Float64, 3}:\n",
+ "[:, :, 1] =\n",
+ " 0.0 9.35498 0.33 0.233874 0.995058 0.761184 0.035101 2.02027\n",
+ " 0.0 9.35498 0.33 0.233874 0.995058 0.761184 0.035101 2.02027\n",
+ " 0.0 9.35498 0.33 0.233874 0.995058 0.761184 0.035101 2.02027\n",
+ " 0.0 9.35498 0.33 0.233874 0.995058 0.761184 0.035101 2.02027\n",
+ " 0.0 9.35498 0.33 0.233874 0.995058 0.761184 0.035101 2.02027\n",
+ " 0.0 9.35498 0.33 0.233874 0.995058 0.761184 0.035101 2.02027\n",
+ " 0.0 9.35498 0.33 0.233874 0.995058 0.761184 0.035101 2.02027\n",
+ " 0.0 9.35498 0.33 0.233874 0.995058 0.761184 0.035101 2.02027\n",
+ " 0.0 9.35498 0.33 0.233874 0.995058 0.761184 0.035101 2.02027\n",
+ " 0.0 9.35498 0.33 0.233874 0.995058 0.761184 0.035101 2.02027\n",
+ "\n",
+ "[:, :, 2] =\n",
+ " -0.0317476 9.35498 0.325518 0.195313 … 0.75986 0.033694 1.96599\n",
+ " 0.0240112 9.35498 0.333389 0.263039 0.763203 0.036201 2.0624\n",
+ " 0.0324415 9.35498 0.334579 0.273278 0.764125 0.0365948 2.07742\n",
+ " -0.031729 9.35498 0.325521 0.195336 0.75986 0.0336949 1.96602\n",
+ " -0.0182628 9.35498 0.327422 0.211692 0.760238 0.0342852 1.98885\n",
+ " -0.0215776 9.35498 0.326954 0.207666 … 0.76012 0.034139 1.9832\n",
+ " 0.0184392 9.35498 0.332603 0.256271 0.762655 0.035943 2.05254\n",
+ " -0.00582174 9.35498 0.329178 0.226803 0.760827 0.034839 2.0102\n",
+ " 0.0213956 9.35498 0.33302 0.259862 0.76294 0.0360797 2.05776\n",
+ " -0.0246001 9.35498 0.326527 0.203995 0.760026 0.0340062 1.97807\n",
+ "\n",
+ "[:, :, 3] =\n",
+ " -0.00915271 9.31642 0.329297 0.223163 … 0.760081 0.0348278 2.00054\n",
+ " 0.003247 9.38414 0.330013 0.237512 0.761834 0.0351427 2.0289\n",
+ " 0.0451613 9.39438 0.335773 0.288314 0.766341 0.0370473 2.10445\n",
+ " -0.0356035 9.31644 0.325563 0.191035 0.759254 0.0336604 1.95567\n",
+ " -0.0237274 9.3328 0.32699 0.205288 0.759733 0.0341224 1.97732\n",
+ " -0.0293077 9.32877 0.326263 0.198552 … 0.759534 0.0338918 1.96748\n",
+ " 0.0174142 9.37737 0.332116 0.254791 0.762896 0.0358135 2.05305\n",
+ " -0.010388 9.34791 0.328642 0.221331 0.760481 0.03466 2.00162\n",
+ " 0.016256 9.38097 0.331898 0.253346 0.762843 0.0357471 2.05137\n",
+ " -0.0307976 9.3251 0.326109 0.196781 0.759451 0.0338395 1.96461\n",
+ "\n",
+ "...\n",
+ "\n",
+ "[:, :, 38] =\n",
+ " 0.0255645 9.6474 0.32914 0.26185 … 0.767595 0.0352133 2.09555\n",
+ " 0.0251415 8.88145 0.340785 0.269392 0.755506 0.0380812 2.015\n",
+ " 0.00959658 9.65565 0.32676 0.242369 0.766139 0.0344676 2.06788\n",
+ " -0.0393856 9.15161 0.327548 0.188175 0.756807 0.0340753 1.93296\n",
+ " -0.0114659 9.6831 0.323368 0.216497 0.765029 0.0334504 2.03367\n",
+ " 0.0250982 9.7418 0.327632 0.260291 … 0.768814 0.0348606 2.1045\n",
+ " 0.000234159 9.46388 0.328369 0.233014 0.762775 0.0347226 2.03179\n",
+ " 0.0228223 9.59504 0.329553 0.25907 0.766575 0.0352748 2.08519\n",
+ " 0.0608539 9.58291 0.335107 0.305392 0.771559 0.0370862 2.15321\n",
+ " 0.00570293 9.14733 0.333978 0.242985 0.758386 0.0361256 2.00887\n",
+ "\n",
+ "[:, :, 39] =\n",
+ " 0.0243632 9.66807 0.328655 0.260174 … 0.767745 0.0350859 2.09553\n",
+ " 0.0238669 8.92881 0.339881 0.267345 0.756222 0.0378301 2.01773\n",
+ " 0.0209962 9.65663 0.328354 0.256204 0.767231 0.0349743 2.0883\n",
+ " -0.0407315 9.111 0.327979 0.186967 0.756189 0.0341611 1.9267\n",
+ " -0.0287066 9.65752 0.321325 0.195825 0.763997 0.0327974 2.00134\n",
+ " 0.0251434 9.75854 0.327382 0.26017 … 0.76904 0.0348043 2.10631\n",
+ " -0.0104649 9.46029 0.326913 0.220056 0.762084 0.0342597 2.01287\n",
+ " 0.0296939 9.61424 0.33023 0.267215 0.767605 0.0355193 2.09954\n",
+ " 0.056847 9.64873 0.333536 0.299833 0.771857 0.0366533 2.15279\n",
+ " -0.021671 9.16163 0.329895 0.209586 0.757247 0.0348251 1.96359\n",
+ "\n",
+ "[:, :, 40] =\n",
+ " 0.0100278 9.68654 0.326349 0.242567 … 0.766587 0.0343798 2.07181\n",
+ " -0.00703566 8.97293 0.334845 0.229347 0.754796 0.0361941 1.9692\n",
+ " 0.0304758 9.67142 0.329466 0.267563 0.768487 0.0353512 2.1069\n",
+ " -0.0624603 9.07019 0.325535 0.161004 0.755908 0.0333599 1.88714\n",
+ " -0.0342069 9.6119 0.321245 0.189624 0.763286 0.0327157 1.98742\n",
+ " 0.0583957 9.77475 0.331829 0.300388 … 0.773869 0.0362674 2.16905\n",
+ " 0.0151781 9.44384 0.330785 0.251376 0.763672 0.0354692 2.05596\n",
+ " 0.0262424 9.64109 0.329332 0.26274 0.767584 0.0352664 2.09611\n",
+ " 0.0659764 9.70734 0.333929 0.310305 0.774232 0.0368687 2.17603\n",
+ " -0.030575 9.14218 0.328936 0.198976 0.756748 0.0344982 1.94669"
+ ]
+ },
+ "metadata": {}
+ }
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "source": [
+ "m0 = model.calibration[:exogenous]"
+ ],
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "1-element Vector{Float64}:\n",
+ " 0.0"
+ ]
+ },
+ "metadata": {}
+ }
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "source": [
+ "s0 = model.calibration[:states]"
+ ],
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "1-element Vector{Float64}:\n",
+ " 9.354978290145986"
+ ]
+ },
+ "metadata": {}
+ }
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "source": [
+ "dr_global.dr(m0, s0)"
+ ],
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "1×2 adjoint(reshape(reinterpret(Float64, ::Vector{StaticArrays.SVector{2, Float64}}), 2, 1)) with eltype Float64:\n",
+ " 0.329904 0.233622"
+ ]
+ },
+ "metadata": {}
+ }
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "source": [
+ "irf = response(model, dr_global.dr, :z)"
+ ],
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "2-dimensional AxisArray{Float64,2,...} with axes:\n",
+ " :V, [:z, :k, :n, :i, :y, :c, :rk, :w]\n",
+ " :T, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10 … 31, 32, 33, 34, 35, 36, 37, 38, 39, 40]\n",
+ "And data, a 8×40 Matrix{Float64}:\n",
+ " 0.0 0.016 0.0128 … 5.1923e-6 4.15384e-6 3.32307e-6\n",
+ " 9.35498 9.35473 9.37411 9.38198 9.38077 9.37961\n",
+ " 0.329904 0.332172 0.331424 0.329493 0.329511 0.329528\n",
+ " 0.233622 0.25325 0.249096 0.233343 0.233354 0.233366\n",
+ " 0.994864 1.01555 1.01147 0.994984 0.994977 0.994972\n",
+ " 0.761243 0.762302 0.762374 … 0.761641 0.761623 0.761606\n",
+ " 0.0350942 0.0358249 0.0356071 0.0349974 0.0350017 0.0350058\n",
+ " 2.02046 2.0484 2.04477 2.02323 2.0231 2.02298"
+ ]
+ },
+ "metadata": {}
+ }
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Let us plot the response of consumption and investment."
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "source": [
+ "p1 = plot()\n",
+ "p2 = plot()\n",
+ "p3 = plot()\n",
+ "p4 = plot()\n",
+ "plot!(p1,1:40, irf[V=:z], legend=false, title = \"Productivity\", xaxis = \"t\")\n",
+ "plot!(p2,1:40, irf[V=:i], legend=false, title = \"Investment\", xaxis = \"t\")\n",
+ "plot!(p3,1:40, irf[V=:n], legend=false, title = \"Labour\", xaxis = \"t\")\n",
+ "plot!(p4,1:40, irf[V=:c], legend=false, title = \"Consumption\", xaxis = \"t\")\n",
+ "\n",
+ "plot(p1,p2,p3,p4, layout = (2,2))"
+ ],
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/html": [
+ "\n",
+ "\n"
+ ],
+ "image/png": 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",
+ "image/svg+xml": "\n\n"
+ },
+ "metadata": {}
+ }
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Stochastic simulations\n",
+ "Now we run 1000 random simulations. The result is an array of size $T\\times N \\times n_v$ where\n",
+ "$T$ the number of dates\n",
+ "$N$ the number of simulations\n",
+ "$n_v$ is the number of variables"
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "source": [
+ "sim = simulate(model, dr_global.dr, N=1000, T=40 )\n",
+ "print(size(sim))"
+ ],
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "(1000, 8, 40)"
+ ]
+ }
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "We plot the responses of consumption, investment and labour to the stochastic path of productivity."
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "source": [
+ "p_z = plot()\n",
+ "p_i = plot()\n",
+ "p_n = plot()\n",
+ "p_c = plot()\n",
+ "for i in 1:1000\n",
+ " plot!(p_z,1:40,sim[N=i][V=:z], color=\"red\",legend=false,alpha=0.1,title = \"Productivity\", xaxis = \"t\")\n",
+ " plot!(p_i,1:40,sim[N=i][V=:i], color=\"red\",legend=false,alpha=0.1,title = \"Investment\", xaxis = \"t\")\n",
+ " plot!(p_n,1:40,sim[N=i][V=:n], color=\"red\",legend=false,alpha=0.1,title = \"Labour\", xaxis = \"t\")\n",
+ " plot!(p_c,1:40,sim[N=i][V=:c], color=\"red\",legend=false,alpha=0.1,title = \"Consumption\", xaxis = \"t\")\n",
+ "end\n",
+ "plot(p_z,p_i,p_c,p_n, layout=(2,2)) "
+ ],
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/html": [
+ "\n",
+ "\n"
+ ],
+ "image/png": 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",
+ "image/svg+xml": "\n\n"
+ },
+ "metadata": {}
+ }
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "We find that while the distribution of investment and labour converges quickly to the ergodic distribution, that of consumption takes noticeably longer. This is indicative of higher persistence in consumption, which in turn could be explained by permanent income considerations."
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "\n",
+ "# Descriptive statistics\n",
+ "A common way to evaluate the success of the RBC model is in its ability to mimic patterns in the descriptive statistics of the real economy. Let us compute some of these descriptive statistics from our sample of stochastic simulations. First we compute growth rates:"
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "source": [
+ "dsim = map(/,sim[T=1:39], sim[T=2:40])"
+ ],
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "3-dimensional AxisArray{Float64,3,...} with axes:\n",
+ " :N, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10 … 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000]\n",
+ " :V, [:z, :k, :n, :i, :y, :c, :rk, :w]\n",
+ " :T, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10 … 30, 31, 32, 33, 34, 35, 36, 37, 38, 39]\n",
+ "And data, a 1000×8×39 Array{Float64, 3}:\n",
+ "[:, :, 1] =\n",
+ " -0.0 1.00003 1.00759 1.09999 1.02302 1.00152 1.02299 1.01532\n",
+ " 0.0 1.00003 0.992232 0.912537 0.976829 0.998417 0.976802 0.984476\n",
+ " -0.0 1.00003 1.0085 1.11309 1.0258 1.0017 1.02578 1.01715\n",
+ " 0.0 1.00003 0.993991 0.931301 0.98207 0.99878 0.982043 0.988007\n",
+ " 0.0 1.00003 0.989831 0.887747 0.969662 0.99792 0.969635 0.979623\n",
+ " -0.0 1.00003 1.00538 1.0693 1.0163 1.00108 1.01628 1.01087\n",
+ " -0.0 1.00003 1.00247 1.03096 1.00749 1.0005 1.00746 1.00501\n",
+ " -0.0 1.00003 1.01472 1.20818 1.04459 1.00292 1.04456 1.02944\n",
+ " -0.0 1.00003 1.00201 1.02505 1.00609 1.00041 1.00606 1.00407\n",
+ " -0.0 1.00003 1.00905 1.12106 1.02747 1.0018 1.02744 1.01825\n",
+ " ⋮ ⋮ \n",
+ " 0.0 1.00003 0.998003 0.976364 0.994047 0.999603 0.99402 0.996035\n",
+ " -0.0 1.00003 1.00423 1.05395 1.01283 1.00085 1.01281 1.00856\n",
+ " -0.0 1.00003 1.00586 1.07591 1.01778 1.00117 1.01775 1.01184\n",
+ " 0.0 1.00003 0.99346 0.925575 0.980487 0.99867 0.98046 0.986942\n",
+ " -0.0 1.00003 1.00391 1.0496 1.01184 1.00079 1.01181 1.0079\n",
+ " -0.0 1.00003 1.00389 1.04937 1.01178 1.00078 1.01176 1.00787\n",
+ " -0.0 1.00003 1.00642 1.08356 1.01946 1.00128 1.01943 1.01296\n",
+ " 0.0 1.00003 0.993401 0.92494 0.98031 0.998658 0.980284 0.986823\n",
+ " 0.0 1.00003 0.998237 0.979099 0.994747 0.99965 0.99472 0.996504\n",
+ "\n",
+ "[:, :, 2] =\n",
+ " 1.21022 1.0023 0.997678 0.98201 … 1.00016 0.993849 0.998455\n",
+ " 13.2661 0.997638 1.00826 1.08913 1.00104 1.02424 1.01345\n",
+ " 0.703117 1.00257 1.00251 1.0479 1.00119 1.00835 1.00841\n",
+ " -10.0169 0.998187 1.00743 1.08248 1.00102 1.02188 1.0125\n",
+ " 0.72578 0.996878 0.997575 0.959619 0.99867 0.991448 0.990756\n",
+ " 0.470128 1.00165 1.00542 1.08238 … 1.00152 1.01682 1.01301\n",
+ " 0.534742 1.00078 1.00182 1.02713 1.00057 1.00576 1.00471\n",
+ " 1.47639 1.00435 0.993332 0.935768 0.999839 0.981861 0.99275\n",
+ " -0.453526 1.00064 0.993277 0.924946 0.998805 0.98013 0.987393\n",
+ " 2.49474 1.00273 0.993417 0.931548 0.99942 0.981311 0.990511\n",
+ " ⋮ ⋱ ⋮ \n",
+ " -0.575346 0.999422 1.0057 1.06905 1.00099 1.01705 1.0107\n",
+ " -1.49661 1.00131 0.992392 0.91661 0.998814 0.977724 0.986506\n",
+ " 2.02267 1.00179 0.996265 0.962418 0.999736 0.989426 0.994914\n",
+ " -0.656092 0.998022 1.01759 1.22842 1.00297 1.05304 1.03278\n",
+ " 3.01345 1.00121 0.996869 0.967471 … 0.999698 0.991019 0.995332\n",
+ " 0.648206 1.0012 1.00159 1.02671 1.00064 1.00522 1.00483\n",
+ " 0.611662 1.00196 1.00326 1.05413 1.00117 1.01043 1.00912\n",
+ " 0.773863 0.998004 0.998943 0.979009 0.999246 0.99612 0.995184\n",
+ " -0.232308 0.999494 1.00956 1.12334 1.00177 1.02884 1.01858\n",
+ "\n",
+ "[:, :, 3] =\n",
+ " 0.622794 1.00183 1.00302 1.05005 … 1.00109 1.00969 1.0085\n",
+ " -2.07455 0.999932 1.0009 1.01067 1.00016 1.00269 1.00172\n",
+ " 2.23678 1.00355 0.991764 0.914232 0.999304 0.976795 0.988401\n",
+ " -4.60457 1.00027 0.999151 0.991014 0.999902 0.997542 0.998662\n",
+ " 1.03806 0.995794 1.00231 1.00703 0.999345 1.00559 0.999061\n",
+ " 0.530534 1.0034 1.00908 1.1559 … 1.00259 1.02782 1.02204\n",
+ " 1.90774 1.0014 0.99719 0.972062 0.999814 0.992048 0.996236\n",
+ " -2.79625 1.00282 0.985575 0.845776 0.997835 0.958168 0.974936\n",
+ " -0.589852 0.998645 1.01267 1.16028 1.00217 1.0381 1.02372\n",
+ " 0.243456 1.00102 1.01098 1.16036 1.00245 1.0334 1.02322\n",
+ " ⋮ ⋱ ⋮ \n",
+ " 1.14646 1.00109 0.999086 0.994011 1.00011 0.997631 0.99963\n",
+ " 0.817218 0.999116 0.999748 0.992898 0.999711 0.998943 0.998311\n",
+ " -0.32021 1.00084 0.98781 0.868134 0.997728 0.96397 0.976684\n",
+ " 6.99849 1.00309 0.990108 0.894563 0.998854 0.97167 0.984409\n",
+ " 0.104466 1.00038 1.01111 1.15539 … 1.0023 1.03373 1.02276\n",
+ " -9.61202 1.0018 0.99262 0.919993 0.998999 0.978557 0.987603\n",
+ " 1.08373 1.0031 0.997824 0.987674 1.0004 0.994588 0.999849\n",
+ " -2.08344 0.997482 1.01383 1.16426 1.0021 1.04128 1.02449\n",
+ " 0.327187 1.00231 1.01516 1.24752 1.00341 1.04579 1.03255\n",
+ "\n",
+ "...\n",
+ "\n",
+ "[:, :, 37] =\n",
+ " 1.51899 1.00946 0.98314 0.803978 … 1.00067 0.954609 0.980163\n",
+ " 1.23574 0.996843 1.00386 1.02964 0.999925 1.01069 1.00362\n",
+ " 0.817884 1.00332 1.00099 1.03151 1.0011 1.00403 1.00637\n",
+ " 2.18455 0.997127 1.0068 1.0668 1.00061 1.01968 1.00988\n",
+ " 1.16032 0.994941 1.00462 1.02891 0.999615 1.01253 1.00277\n",
+ " 0.834159 1.00033 0.999365 0.99402 … 0.999962 0.998221 0.999182\n",
+ " 1.50413 1.00507 0.990682 0.903099 0.999551 0.974425 0.988572\n",
+ " 0.396655 1.00182 1.01005 1.15475 1.00248 1.03065 1.02226\n",
+ " 0.0552351 1.00082 0.983957 0.833896 0.997005 0.952678 0.969008\n",
+ " -0.187983 1.00095 0.994384 0.938986 0.999134 0.983634 0.990128\n",
+ " ⋮ ⋱ ⋮ \n",
+ " 4.25294 0.997272 1.00553 1.05342 1.00042 1.01592 1.00758\n",
+ " -0.40193 0.998882 0.997502 0.965206 0.99917 0.992009 0.993381\n",
+ " 0.281883 0.998505 0.997436 0.963399 0.999054 0.991708 0.992771\n",
+ " -8.10568 0.997555 1.01587 1.18873 1.00242 1.04722 1.02834\n",
+ " 0.538609 0.995412 0.994116 0.917619 … 0.997468 0.979926 0.981204\n",
+ " 0.229111 1.00003 0.994411 0.936604 0.99888 0.983362 0.988922\n",
+ " 0.28504 0.997684 0.99547 0.938641 0.998397 0.985382 0.987573\n",
+ " 0.801379 1.00353 1.00151 1.0403 1.00125 1.00562 1.00764\n",
+ " 4.21963 1.001 0.98924 0.874374 0.997893 0.967531 0.979033\n",
+ "\n",
+ "[:, :, 38] =\n",
+ " 0.921801 1.0054 0.999699 1.02855 … 1.00127 1.00089 1.0066\n",
+ " 0.527758 0.997735 0.99166 0.902505 0.997644 0.973756 0.979722\n",
+ " 1.60303 1.00392 0.993288 0.933745 0.999713 0.98151 0.992018\n",
+ " 0.912049 0.998941 1.0 0.995129 0.999716 0.999653 0.998591\n",
+ " 0.818313 0.995925 0.998351 0.964852 0.998531 0.993391 0.990977\n",
+ " 0.322024 1.00017 0.993724 0.930285 … 0.998795 0.981425 0.987792\n",
+ " 7.71662 1.00279 0.986981 0.85671 0.998033 0.962033 0.977447\n",
+ " 4.54382 1.00491 0.984118 0.830076 0.998126 0.955084 0.975266\n",
+ " 0.820533 0.995999 0.998182 0.963584 0.998498 0.992905 0.990734\n",
+ " -0.345882 0.999361 1.01761 1.24304 1.00326 1.0531 1.03421\n",
+ " ⋮ ⋱ ⋮ \n",
+ " 1.99215 0.998745 1.00121 1.00853 0.999911 1.00323 1.00076\n",
+ " 0.608006 0.997974 0.999499 0.984709 0.999343 0.997762 0.99624\n",
+ " 1.76828 0.997544 1.00301 1.02404 0.999966 1.0083 1.00281\n",
+ " -0.728827 1.00197 0.995298 0.953247 0.999613 0.986769 0.993382\n",
+ " 1.36597 0.992931 1.00757 1.05376 … 0.99973 1.0213 1.00646\n",
+ " 2.70062 0.998346 1.0053 1.0554 1.00062 1.01538 1.00837\n",
+ " 0.658714 0.995976 0.997767 0.957615 0.998432 0.991646 0.989866\n",
+ " 1.35032 1.00429 0.994152 0.945642 0.999986 0.984248 0.994289\n",
+ " -1.32463 0.99753 0.99548 0.935558 0.99831 0.985059 0.987087\n",
+ "\n",
+ "[:, :, 39] =\n",
+ " 1.72466 1.00585 0.986479 0.850912 … 0.999133 0.962442 0.981339\n",
+ " 1.12995 0.994879 1.00445 1.02635 0.999586 1.01197 1.00232\n",
+ " 1.34812 1.00233 0.996872 0.97191 1.0 0.991444 0.996869\n",
+ " -1.14781 0.998841 1.01025 1.12545 1.00172 1.03061 1.01897\n",
+ " 1.62073 0.994997 1.00941 1.08495 1.0006 1.02742 1.01275\n",
+ " -4.20136 0.998309 1.0122 1.14539 … 1.00194 1.03622 1.02201\n",
+ " 2.9582 0.998999 0.999274 0.986341 0.999576 0.997422 0.997148\n",
+ " -0.389263 1.00065 0.986194 0.850698 0.997307 0.958942 0.973002\n",
+ " 1.16488 0.995032 1.00493 1.03235 0.999728 1.01349 1.00351\n",
+ " 0.916178 1.00441 0.999258 1.01391 1.00104 0.99925 1.0044\n",
+ " ⋮ ⋱ ⋮ \n",
+ " 0.041937 0.998999 0.985444 0.841678 0.996654 0.955646 0.968791\n",
+ " 0.279498 0.997611 0.99213 0.903915 0.997675 0.97516 0.980547\n",
+ " 1.28204 0.998251 1.00131 1.00733 0.999801 1.00338 1.00031\n",
+ " -0.363999 1.00079 1.00804 1.10923 1.00175 1.0243 1.01693\n",
+ " 0.808347 0.994762 0.999274 0.969318 … 0.998426 0.995741 0.991245\n",
+ " -0.600586 0.999784 1.00729 1.0922 1.00138 1.02189 1.01427\n",
+ " 0.571693 0.994824 0.993768 0.912786 0.997155 0.978466 0.979505\n",
+ " 0.717153 1.003 1.00317 1.0606 1.00145 1.01039 1.01021\n",
+ " 0.617765 0.995722 1.00033 0.984196 0.998896 0.999457 0.994848"
+ ]
+ },
+ "metadata": {}
+ }
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Then we compute the volatility of growth rates for each simulation:"
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "source": [
+ "model.calibration"
+ ],
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "ModelCalibration(FlatCalibration(:sigma => 5.0, :zbar => 0.0, :beta => 0.99, :eta => 1.0, :n => 0.33, :alpha => 0.33, :sig_z => 0.016, :rho => 0.8, :delta => 0.025, :m => 0.0…), GroupedCalibration(:values => [-27.288057500907858], :controls => [0.33, 0.23387445725364966], :states => [9.354978290145986], :exogenous => [0.0], :rewards => [-2.0492067865980133], :expectations => [0.0], :parameters => [0.99, 5.0, 1.0, 23.95785990938192, 0.025, 0.33, 0.8, 0.0, 0.016]), Dict(:alpha => (:parameters, 6), :n => (:controls, 1), :rho => (:parameters, 7), :V => (:values, 1), :delta => (:parameters, 5), :eta => (:parameters, 3), :sigma => (:parameters, 2), :k => (:states, 1), :sig_z => (:parameters, 9), :z => (:exogenous, 1)…), OrderedCollections.OrderedDict(:exogenous => [:z], :states => [:k], :controls => [:n, :i], :expectations => [:m], :values => [:V], :parameters => [:beta, :sigma, :eta, :chi, :delta, :alpha, :rho, :zbar, :sig_z], :rewards => [:u]))"
+ ]
+ },
+ "metadata": {}
+ }
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "source": [
+ "#mean(dsim[T=1,V=:k])\n",
+ "2+2"
+ ],
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "4"
+ ]
+ },
+ "metadata": {}
+ }
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "volat"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Then we compute the mean and a confidence interval for each variable. In the generated table the first column contains the standard deviations of growth rates. The second and third columns contain the lower and upper bounds of the 95% confidence intervals, respectively."
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "table = np.column_stack([\n",
+ " volat.mean(axis=0),\n",
+ " volat.mean(axis=0)-1.96*volat.std(axis=0),\n",
+ " volat.mean(axis=0)+1.96*volat.std(axis=0) ])\n",
+ "table"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "We can use the pandas library to present the results in a nice table."
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "import pandas\n",
+ "df = pandas.DataFrame(table, index=sim.V, \n",
+ " columns=['Growth rate std.',\n",
+ " 'Lower 95% bound',\n",
+ " 'Upper 95% bound' ])\n",
+ "pandas.set_option('precision', 4)\n",
+ "df"
+ ],
+ "outputs": [],
+ "metadata": {}
+ }
+ ],
+ "metadata": {
+ "orig_nbformat": 4,
+ "language_info": {
+ "name": "julia",
+ "version": "1.6.3",
+ "mimetype": "application/julia",
+ "file_extension": ".jl"
+ },
+ "kernelspec": {
+ "display_name": "Julia 1.6.3",
+ "language": "julia",
+ "name": "julia-1.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file