{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from matplotlib import pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Solving the rbc model\n", "\n", "This worksheet demonstrates how to solve the RBC model with the [dolo](http://econforge.github.io/dolo/) library \n", "and how to generates impulse responses and stochastic simulations from the solution.\n", "\n", "- This notebook is distributed with dolo in : ``examples\\notebooks\\``. The notebook was opened and run from that directory.\n", "- The model file is in : ``examples\\global_models\\``\n", "\n", "First we import the dolo library." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from dolo import *" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "breakpoint": false } }, "source": [ "The RBC model is defined in a [YAML](http://www.yaml.org/spec/1.2/spec.html#Introduction) file which we can read locally or pull off the web." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "run_control": { "breakpoint": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "name: Real Business Cycle\n", "\n", "symbols:\n", "\n", " exogenous: [e_z]\n", " states: [z, k]\n", " controls: [n, i]\n", " expectations: [m]\n", " values: [V]\n", " parameters: [beta, sigma, eta, chi, delta, alpha, rho, zbar, sig_z]\n", " rewards: [u]\n", "\n", "definitions:\n", " y: exp(z)*k^alpha*n^(1-alpha)\n", " c: y - i\n", " rk: alpha*y/k\n", " w: (1-alpha)*y/n\n", "\n", "equations:\n", "\n", " arbitrage:\n", " - chi*n^eta*c^sigma - w | 0.0 <= n <= inf\n", " - 1 - beta*(c/c(1))^(sigma)*(1-delta+rk(1)) | 0.0 <= i <= inf\n", "\n", "\n", " transition:\n", " - z = rho*z(-1) + e_z\n", " - k = (1-delta)*k(-1) + i(-1)\n", "\n", " value:\n", " - V = c^(1-sigma)/(1-sigma) - chi*n^(1+eta)/(1+eta) + beta*V(1)\n", "\n", " felicity:\n", " - u = c^(1-sigma)/(1-sigma) - chi*n^(1+eta)/(1+eta)\n", "\n", " expectation:\n", " - m = beta/c(1)^sigma*(1-delta+rk(1))\n", "\n", " direct_response:\n", " - n = ((1-alpha)*exp(z)*k^alpha*m/chi)^(1/(eta+alpha))\n", " - i = exp(z)*k^alpha*n^(1-alpha) - (m)^(-1/sigma)\n", "\n", "calibration:\n", "\n", " # parameters\n", " beta : 0.99\n", " phi: 1\n", " delta : 0.025\n", " alpha : 0.33\n", " rho : 0.8\n", " sigma: 5\n", " eta: 1\n", " sig_z: 0.016\n", " zbar: 0\n", " chi : w/c^sigma/n^eta\n", " c_i: 1.5\n", " c_y: 0.5\n", " e_z: 0.0\n", "\n", " # endogenous variables\n", " n: 0.33\n", " z: zbar\n", " rk: 1/beta-1+delta\n", " w: (1-alpha)*exp(z)*(k/n)^(alpha)\n", " k: n/(rk/alpha)^(1/(1-alpha))\n", " y: exp(z)*k^alpha*n^(1-alpha)\n", " i: delta*k\n", " c: y - i\n", " V: log(c)/(1-beta)\n", " u: c^(1-sigma)/(1-sigma) - chi*n^(1+eta)/(1+eta)\n", " m: beta/c^sigma*(1-delta+rk)\n", "\n", "exogenous: !Normal\n", " Sigma: [[sig_z**2]]\n", "\n", "domain:\n", " z: [-2*sig_z/(1-rho^2)^0.5, 2*sig_z/(1-rho^2)^0.5]\n", " k: [ k*0.5, k*1.5]\n", "\n", "options:\n", " grid: !Cartesian\n", " n: [20, 20]\n", "# options:\n", "# grid: !Smolyak\n", "# mu: 3\n", "# # orders: [5, 50]\n" ] } ], "source": [ "# filename = ('https://raw.githubusercontent.com/EconForge/dolo'\n", "# '/master/examples/models/compat/rbc.yaml')\n", "\n", "filename='../models/rbc.yaml'\n", "\n", "%cat $filename" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "breakpoint": false } }, "source": [ "`yaml_import(filename)` reads the YAML file and generates a model object. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "model = yaml_import(filename)" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "breakpoint": false } }, "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." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'transition': array([0., 0.]),\n", " 'arbitrage': array([-4.4408921e-16, 0.0000000e+00])}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.residuals()" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "breakpoint": false } }, "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." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " Model:\n", " ------\n", " name: \"Real Business Cycle\"\n", " type: \"dtcc\"\n", " file: \"../models/rbc.yaml\n", "\n", "Equations:\n", "----------\n", "\n", "transition\n", " 1 : 0.0000 : z(0) == (rho) * (z(-(1))) + e_z(0)\n", " 2 : 0.0000 : k(0) == ((1) - (delta)) * (k(-(1))) + i(-(1))\n", "\n", "arbitrage\n", " 1 : 0.0000 : (((chi) * ((n(0)) ** (eta))) * ((c(0)) ** (sigma))) - (w(0))\n", " 2 : 0.0000 : (1) - (((beta) * (((c(0)) / (c(1))) ** (sigma))) * ((1) - (delta) + rk(1)))\n", "\n", "definitions\n", " 1 : y = exp(z)*k**alpha*n**(1-alpha)\n", " 2 : c = y - i\n", " 3 : rk = alpha*y/k\n", " 4 : w = (1-alpha)*y/n\n", "\n", "\n" ] } ], "source": [ "print( model ) " ] }, { "cell_type": "markdown", "metadata": { "run_control": { "breakpoint": false } }, "source": [ "Next we compute a solution to the model using a first order perturbation method (see the source for the [approximate_controls](https://github.com/EconForge/dolo/blob/master/dolo/algos/perturbation.py) 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](https://github.com/EconForge/dolo/blob/master/dolo/numeric/taylor_expansion.py) class)." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "run_control": { "breakpoint": false } }, "outputs": [], "source": [ "dr_pert = perturb(model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now compute the global solution (see the source for the [time_iteration](https://github.com/EconForge/dolo/blob/master/dolo/algos/time_iteration.py) function). It returns a decision rule object of type SmolyakGrid (see the source for the [SmolyakGrid](https://github.com/EconForge/dolo/blob/master/dolo/numeric/interpolation/smolyak.py) class)." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Solving WITH complementarities.\n", "------------------------------------------------\n", "| N | Error | Gain | Time | nit |\n", "------------------------------------------------\n", "| 1 | 6.178e-01 | nan | 1.744 | 8 |\n", "| 2 | 1.571e-01 | 0.254 | 0.052 | 5 |\n", "| 3 | 1.019e-01 | 0.649 | 0.053 | 5 |\n", "| 4 | 7.094e-02 | 0.696 | 0.042 | 4 |\n", "| 5 | 5.213e-02 | 0.735 | 0.044 | 4 |\n", "| 6 | 3.972e-02 | 0.762 | 0.050 | 4 |\n", "| 7 | 3.108e-02 | 0.783 | 0.043 | 4 |\n", "| 8 | 2.484e-02 | 0.799 | 0.044 | 4 |\n", "| 9 | 2.019e-02 | 0.813 | 0.043 | 4 |\n", "| 10 | 1.664e-02 | 0.824 | 0.044 | 4 |\n", "| 11 | 1.387e-02 | 0.834 | 0.035 | 3 |\n", "| 12 | 1.169e-02 | 0.842 | 0.035 | 3 |\n", "| 13 | 9.931e-03 | 0.850 | 0.034 | 3 |\n", "| 14 | 8.692e-03 | 0.875 | 0.033 | 3 |\n", "| 15 | 7.784e-03 | 0.896 | 0.032 | 3 |\n", "| 16 | 6.989e-03 | 0.898 | 0.033 | 3 |\n", "| 17 | 6.293e-03 | 0.900 | 0.033 | 3 |\n", "| 18 | 5.682e-03 | 0.903 | 0.036 | 3 |\n", "| 19 | 5.144e-03 | 0.905 | 0.035 | 3 |\n", "| 20 | 4.669e-03 | 0.908 | 0.031 | 3 |\n", "| 21 | 4.247e-03 | 0.910 | 0.033 | 3 |\n", "| 22 | 3.870e-03 | 0.911 | 0.031 | 3 |\n", "| 23 | 3.534e-03 | 0.913 | 0.032 | 3 |\n", "| 24 | 3.231e-03 | 0.914 | 0.040 | 3 |\n", "| 25 | 2.958e-03 | 0.916 | 0.032 | 3 |\n", "| 26 | 2.711e-03 | 0.917 | 0.034 | 3 |\n", "| 27 | 2.488e-03 | 0.918 | 0.033 | 3 |\n", "| 28 | 2.285e-03 | 0.918 | 0.024 | 2 |\n", "| 29 | 2.100e-03 | 0.919 | 0.028 | 2 |\n", "| 30 | 1.931e-03 | 0.920 | 0.023 | 2 |\n", "| 31 | 1.777e-03 | 0.920 | 0.022 | 2 |\n", "| 32 | 1.636e-03 | 0.921 | 0.029 | 2 |\n", "| 33 | 1.507e-03 | 0.921 | 0.023 | 2 |\n", "| 34 | 1.388e-03 | 0.921 | 0.023 | 2 |\n", "| 35 | 1.280e-03 | 0.922 | 0.024 | 2 |\n", "| 36 | 1.180e-03 | 0.922 | 0.031 | 2 |\n", "| 37 | 1.088e-03 | 0.922 | 0.031 | 2 |\n", "| 38 | 1.003e-03 | 0.922 | 0.023 | 2 |\n", "| 39 | 9.249e-04 | 0.922 | 0.026 | 2 |\n", "| 40 | 8.531e-04 | 0.922 | 0.030 | 2 |\n", "| 41 | 7.868e-04 | 0.922 | 0.026 | 2 |\n", "| 42 | 7.257e-04 | 0.922 | 0.023 | 2 |\n", "| 43 | 6.694e-04 | 0.922 | 0.022 | 2 |\n", "| 44 | 6.174e-04 | 0.922 | 0.025 | 2 |\n", "| 45 | 5.693e-04 | 0.922 | 0.023 | 2 |\n", "| 46 | 5.250e-04 | 0.922 | 0.022 | 2 |\n", "| 47 | 4.841e-04 | 0.922 | 0.022 | 2 |\n", "| 48 | 4.463e-04 | 0.922 | 0.021 | 2 |\n", "| 49 | 4.114e-04 | 0.922 | 0.033 | 2 |\n", "| 50 | 3.792e-04 | 0.922 | 0.025 | 2 |\n", "| 51 | 3.494e-04 | 0.922 | 0.021 | 2 |\n", "| 52 | 3.219e-04 | 0.921 | 0.023 | 2 |\n", "| 53 | 2.966e-04 | 0.921 | 0.026 | 2 |\n", "| 54 | 2.731e-04 | 0.921 | 0.024 | 2 |\n", "| 55 | 2.515e-04 | 0.921 | 0.025 | 2 |\n", "| 56 | 2.315e-04 | 0.920 | 0.022 | 2 |\n", "| 57 | 2.130e-04 | 0.920 | 0.022 | 2 |\n", "| 58 | 1.959e-04 | 0.920 | 0.030 | 2 |\n", "| 59 | 1.802e-04 | 0.920 | 0.024 | 2 |\n", "| 60 | 1.657e-04 | 0.919 | 0.025 | 2 |\n", "| 61 | 1.523e-04 | 0.919 | 0.022 | 2 |\n", "| 62 | 1.399e-04 | 0.919 | 0.024 | 2 |\n", "| 63 | 1.285e-04 | 0.918 | 0.026 | 2 |\n", "| 64 | 1.180e-04 | 0.918 | 0.021 | 2 |\n", "| 65 | 1.083e-04 | 0.918 | 0.029 | 2 |\n", "| 66 | 9.932e-05 | 0.917 | 0.035 | 2 |\n", "| 67 | 9.107e-05 | 0.917 | 0.027 | 2 |\n", "| 68 | 8.347e-05 | 0.917 | 0.024 | 2 |\n", "| 69 | 7.647e-05 | 0.916 | 0.026 | 2 |\n", "| 70 | 7.002e-05 | 0.916 | 0.023 | 2 |\n", "| 71 | 6.408e-05 | 0.915 | 0.024 | 2 |\n", "| 72 | 5.862e-05 | 0.915 | 0.024 | 2 |\n", "| 73 | 5.359e-05 | 0.914 | 0.022 | 2 |\n", "| 74 | 4.896e-05 | 0.914 | 0.022 | 2 |\n", "| 75 | 4.471e-05 | 0.913 | 0.031 | 2 |\n", "| 76 | 4.079e-05 | 0.912 | 0.016 | 1 |\n", "| 77 | 3.720e-05 | 0.912 | 0.016 | 1 |\n", "| 78 | 3.390e-05 | 0.911 | 0.028 | 1 |\n", "| 79 | 3.087e-05 | 0.911 | 0.018 | 1 |\n", "| 80 | 2.809e-05 | 0.910 | 0.027 | 1 |\n", "| 81 | 2.554e-05 | 0.909 | 0.013 | 1 |\n", "| 82 | 2.319e-05 | 0.908 | 0.017 | 1 |\n", "| 83 | 2.105e-05 | 0.907 | 0.014 | 1 |\n", "| 84 | 1.908e-05 | 0.907 | 0.016 | 1 |\n", "| 85 | 1.728e-05 | 0.906 | 0.015 | 1 |\n", "| 86 | 1.563e-05 | 0.904 | 0.014 | 1 |\n", "| 87 | 1.412e-05 | 0.903 | 0.021 | 1 |\n", "| 88 | 1.274e-05 | 0.902 | 0.023 | 1 |\n", "| 89 | 1.147e-05 | 0.901 | 0.025 | 1 |\n", "| 90 | 1.032e-05 | 0.899 | 0.015 | 1 |\n", "| 91 | 9.266e-06 | 0.898 | 0.015 | 1 |\n", "| 92 | 8.305e-06 | 0.896 | 0.013 | 1 |\n", "| 93 | 7.428e-06 | 0.894 | 0.014 | 1 |\n", "| 94 | 6.629e-06 | 0.892 | 0.016 | 1 |\n", "| 95 | 5.902e-06 | 0.890 | 0.013 | 1 |\n", "| 96 | 5.268e-06 | 0.893 | 0.015 | 1 |\n", "| 97 | 4.926e-06 | 0.935 | 0.014 | 1 |\n", "| 98 | 4.607e-06 | 0.935 | 0.014 | 1 |\n", "| 99 | 4.311e-06 | 0.936 | 0.015 | 1 |\n", "| 100 | 4.035e-06 | 0.936 | 0.016 | 1 |\n", "| 101 | 3.779e-06 | 0.936 | 0.018 | 1 |\n", "| 102 | 3.545e-06 | 0.938 | 0.016 | 1 |\n", "| 103 | 3.327e-06 | 0.939 | 0.014 | 1 |\n", "| 104 | 3.124e-06 | 0.939 | 0.016 | 1 |\n", "| 105 | 2.933e-06 | 0.939 | 0.014 | 1 |\n", "| 106 | 2.756e-06 | 0.940 | 0.013 | 1 |\n", "| 107 | 2.595e-06 | 0.941 | 0.017 | 1 |\n", "| 108 | 2.444e-06 | 0.942 | 0.014 | 1 |\n", "| 109 | 2.302e-06 | 0.942 | 0.014 | 1 |\n", "| 110 | 2.170e-06 | 0.942 | 0.013 | 1 |\n", "| 111 | 2.045e-06 | 0.943 | 0.013 | 1 |\n", "| 112 | 1.929e-06 | 0.943 | 0.018 | 1 |\n", "| 113 | 1.819e-06 | 0.943 | 0.015 | 1 |\n", "| 114 | 1.720e-06 | 0.945 | 0.019 | 1 |\n", "| 115 | 1.626e-06 | 0.946 | 0.017 | 1 |\n", "| 116 | 1.538e-06 | 0.946 | 0.013 | 1 |\n", "| 117 | 1.456e-06 | 0.946 | 0.014 | 1 |\n", "| 118 | 1.380e-06 | 0.948 | 0.013 | 1 |\n", "| 119 | 1.309e-06 | 0.948 | 0.014 | 1 |\n", "| 120 | 1.241e-06 | 0.948 | 0.015 | 1 |\n", "| 121 | 1.178e-06 | 0.949 | 0.015 | 1 |\n", "| 122 | 1.119e-06 | 0.950 | 0.014 | 1 |\n", "| 123 | 1.064e-06 | 0.951 | 0.015 | 1 |\n", "| 124 | 1.012e-06 | 0.951 | 0.014 | 1 |\n", "| 125 | 9.631e-07 | 0.952 | 0.013 | 1 |\n", "------------------------------------------------\n", "Elapsed: 4.7361907958984375 seconds.\n", "------------------------------------------------\n" ] } ], "source": [ "dr_global = time_iteration(model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Decision rule\n", "\n", "Here we plot optimal investment and labour for different levels of capital (see the source for the [plot_decision_rule](https://github.com/EconForge/dolo/blob/master/dolo/algos/simulations.py) function)." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "tab_global = tabulate(model, dr_global, 'k')\n", "tab_pert = tabulate(model, dr_pert, 'k')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from matplotlib import pyplot as plt\n", "\n", "plt.figure(figsize=(8,3.5))\n", "\n", "plt.subplot(121)\n", "plt.plot(tab_global['k'], tab_global['i'], label='Global')\n", "plt.plot(tab_pert['k'], tab_pert['i'], label='Perturbation')\n", "plt.ylabel('i')\n", "plt.title('Investment')\n", "plt.legend()\n", "\n", "plt.subplot(122)\n", "plt.plot(tab_global['k'], tab_global['n'], label='Global')\n", "plt.plot(tab_pert['k'], tab_pert['n'], label='Perturbation')\n", "plt.ylabel('n')\n", "plt.title('Labour')\n", "plt.legend()\n", "\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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", "\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." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "original_delta = model.calibration['delta'] \n", "\n", "drs = []\n", "delta_values = np.linspace(0.01, 0.04,5)\n", "for val in delta_values:\n", " model.set_calibration(delta=val)\n", " drs.append(perturb(model))\n", "\n", " \n", "plt.figure(figsize=(5,3))\n", "\n", "for i,dr in enumerate(drs):\n", " sim = tabulate(model, dr,'k')\n", " plt.plot(sim['k'],sim['i'], label='$\\delta={}$'.format(delta_values[i]))\n", "plt.ylabel('i')\n", "plt.title('Investment')\n", "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", "\n", "model.set_calibration(delta=original_delta)" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "breakpoint": false } }, "source": [ "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)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Use the model to simulate" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "breakpoint": false } }, "source": [ "We will use the deterministic steady-state as a starting point." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "run_control": { "breakpoint": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['z', 'k']=[ 0. 21.49753444]\n" ] } ], "source": [ "s0 = model.calibration['states']\n", "print(str(model.symbols['states'])+'='+str(s0))" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "breakpoint": false } }, "source": [ "We also get the covariance matrix just in case. This is a one shock model so all we have is the variance of $e_z$." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0.000256]])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sigma2_ez = model.exogenous.Sigma\n", "sigma2_ez" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Impulse response functions\n", "\n", "Consider a 10% shock to productivity." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['z', 'k']=[ 0. 21.49753444]\n" ] } ], "source": [ "s1 = s0.copy()\n", "s1[0] *= 1.1\n", "print(str(model.symbols['states'])+'='+str(s1))" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "breakpoint": false } }, "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](https://github.com/EconForge/dolo/blob/master/dolo/algos/simulations.py) function). \n", "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." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "array([[[-8.215569e-03, 0.000000e+00, ..., nan, nan],\n", " [-3.052676e-02, 0.000000e+00, ..., nan, nan],\n", " ...,\n", " [ 5.492295e-03, 0.000000e+00, ..., nan, nan],\n", " [-1.727065e-03, 0.000000e+00, ..., nan, nan]],\n", "\n", " [[ 3.441022e-02, -8.215569e-03, ..., nan, nan],\n", " [-2.093615e-02, -3.052676e-02, ..., nan, nan],\n", " ...,\n", " [-7.392879e-03, 5.492295e-03, ..., nan, nan],\n", " [ 2.368904e-02, -1.727065e-03, ..., nan, nan]],\n", "\n", " ...,\n", "\n", " [[-1.266916e-02, 1.287402e-02, ..., 5.953791e-03, 4.934772e+00],\n", " [ 2.485331e-02, -5.501611e-02, ..., 6.720710e-03, 4.200902e+00],\n", " ...,\n", " [-2.854673e-02, -4.841336e-02, ..., 6.531119e-03, 4.302724e+00],\n", " [-1.180261e-02, 8.695070e-03, ..., 6.717241e-03, 4.621156e+00]],\n", "\n", " [[ 2.503607e-03, -2.369939e-03, ..., 5.881041e-03, 4.853061e+00],\n", " [-1.042596e-02, -1.915958e-02, ..., 7.586131e-03, 4.175176e+00],\n", " ...,\n", " [ 8.016865e-03, -6.727741e-02, ..., 6.498920e-03, 4.193465e+00],\n", " [-4.248650e-03, -4.846552e-03, ..., 6.656005e-03, 4.549168e+00]]])\n", "Coordinates:\n", " * T (T) int64 0 1 2 3 4 5 6 7 8 9 10 ... 30 31 32 33 34 35 36 37 38 39\n", " * N (N) int64 0 1 2 3 4 5 6 7 8 9\n", " * V (V) " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,4))\n", "plt.subplot(221)\n", "plt.plot(irf.sel(V='z'))\n", "plt.title('Productivity')\n", "plt.grid()\n", "plt.subplot(222)\n", "plt.plot(irf.sel(V='i'))\n", "plt.title('Investment')\n", "plt.grid()\n", "plt.subplot(223)\n", "plt.plot(irf.sel(V='n'))\n", "plt.grid()\n", "plt.title('Labour')\n", "plt.subplot(224)\n", "plt.plot(irf.sel(V='c'))\n", "plt.title('Consumption')\n", "plt.grid()\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the plotting is made using the wonderful [matplotlib](http://matplotlib.org/users/pyplot_tutorial.html) library. Read the online [tutorials](http://matplotlib.org/users/beginner.html) to learn how to customize the plots to your needs (e.g., using [latex](http://matplotlib.org/users/usetex.html) in annotations). If instead you would like to produce charts in Matlab, you can easily export the impulse response functions, or any other matrix, to a `.mat` file." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[33mFutureWarning\u001b[0m:/opt/pablo/anaconda3/lib/python3.6/site-packages/altair/utils/core.py:294\n", " A future version of pandas will default to `skipna=True`. To silence this warning, pass `skipna=True|False` explicitly.\n" ] }, { "data": { "application/vnd.vegalite.v2+json": { "$schema": "https://vega.github.io/schema/vega-lite/v2.6.0.json", "config": { "view": { "height": 300, "width": 400 } }, "datasets": { "data-75732e9ef17ffb9f4ae7edc81e4321a1": [ { "T": 0, "c": 0.8422747356706476, "e_z": 0, "i": 0.11039930202861871, "k": 21.49753444140151, "n": 0.20526858994876307, "rk": 0.014624115770006508, "w": 3.109543478706761, "y": 0.9526740376992663, "z": 0 }, { "T": 1, "c": 0.8427134496262819, "e_z": 0.016, "i": 0.11146248809214863, "k": 21.392958399016113, "n": 0.20624654783812896, "rk": 0.014718771175732464, "w": 3.0996779581159175, "y": 0.9541759377184306, "z": 0 }, { "T": 2, "c": 0.8427866813439518, "e_z": 0, "i": 0.13425870442216656, "k": 21.2904913031181, "n": 0.20911979788132457, "rk": 0.01514408346488455, "w": 3.130360755392448, "y": 0.9770453857661184, "z": 0.016 }, { "T": 3, "c": 0.843175290096964, "e_z": 0, "i": 0.130665160206569, 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", "text/plain": [ "\n", "\n", "If you see this message, it means the renderer has not been properly enabled\n", "for the frontend that you are using. For more information, see\n", "https://altair-viz.github.io/user_guide/troubleshooting.html\n" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# it is also possible (and fun) to use the graph visualization altair lib instead:\n", "# it is not part of dolo dependencies. To install `conda install -c conda-forge altair`\n", "import altair as alt\n", "df = irf.drop('N').to_pandas().reset_index() # convert to flat database\n", "base = alt.Chart(df).mark_line()\n", "ch1 = base.encode(x='T', y='z')\n", "ch2 = base.encode(x='T', y='i')\n", "ch3 = base.encode(x='T', y='n')\n", "ch4 = base.encode(x='T', y='c')\n", "(ch1|ch2)& \\\n", "(ch3|ch4)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "irf_array = np.array( irf )\n", "import scipy.io\n", "scipy.io.savemat(\"export.mat\", {'table': irf_array} )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Stochastic simulations\n", "\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\n" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(40, 1000, 9)\n" ] } ], "source": [ "sim = simulate(model, dr_global, N=1000, T=40 )\n", "print(sim.shape)" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "breakpoint": false } }, "source": [ "We plot the responses of consumption, investment and labour to the stochastic path of productivity." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[33mMatplotlibDeprecationWarning\u001b[0m:/opt/pablo/anaconda3/lib/python3.6/site-packages/matplotlib/figure.py:98\n", " \n", "Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,4))\n", "for i in range(1000):\n", " plt.subplot(221)\n", " plt.plot(sim.sel(N=i,V='z'), color='red', alpha=0.1)\n", " plt.subplot(222)\n", " plt.plot(sim.sel(N=i,V='i'), color='red', alpha=0.1)\n", " plt.subplot(223)\n", " plt.plot(sim.sel(N=i,V='n'), color='red', alpha=0.1)\n", " plt.subplot(224)\n", " plt.plot(sim.sel(N=i,V='c'), color='red', alpha=0.1)\n", "\n", "plt.subplot(221)\n", "plt.title('Productivity')\n", "plt.subplot(222)\n", "plt.title('Investment')\n", "plt.subplot(223)\n", "plt.title('Labour')\n", "plt.subplot(224)\n", "plt.title('Consumption')\n", "\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "breakpoint": false } }, "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." ] }, { "cell_type": "markdown", "metadata": { "run_control": { "breakpoint": false } }, "source": [ "# 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:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "run_control": { "breakpoint": false } }, "outputs": [], "source": [ "dsim = sim / sim.shift(T=1)" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "breakpoint": false } }, "source": [ "Then we compute the volatility of growth rates for each simulation:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "run_control": { "breakpoint": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(40, 9)\n" ] } ], "source": [ "volat = dsim.std(axis=1)\n", "print(volat.shape)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "array([[ nan, nan, nan, ..., nan,\n", " nan, nan],\n", " [1.610460e+01, nan, 7.216450e-15, ..., 4.263814e-04,\n", " 2.191723e-02, 1.264240e-02],\n", " [1.618588e+01, 1.610460e+01, 9.880539e-04, ..., 4.002167e-04,\n", " 2.324016e-02, 1.308931e-02],\n", " ...,\n", " [1.354310e+01, 3.780695e+01, 1.826828e-03, ..., 3.710962e-04,\n", " 2.382253e-02, 1.339413e-02],\n", " [3.541631e+01, 1.094721e+01, 1.790904e-03, ..., 3.681102e-04,\n", " 2.234791e-02, 1.265644e-02],\n", " [7.612170e+01, 6.075938e+01, 1.784252e-03, ..., 3.928456e-04,\n", " 2.346935e-02, 1.335695e-02]])\n", "Coordinates:\n", " * T (T) int64 0 1 2 3 4 5 6 7 8 9 10 ... 30 31 32 33 34 35 36 37 38 39\n", " * V (V) \n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Growth rate std.Lower 95% boundUpper 95% bound
V
e_z60.0442-5.5308e+01175.3960
z102.1379-3.7611e+02580.3820
k0.00161.0269e-030.0022
n0.00999.3426e-030.0106
i0.2874-3.1002e-010.8848
y0.02322.2066e-020.0244
c0.00038.1291e-050.0004
rk0.02392.2621e-020.0252
w0.01341.2714e-020.0141
\n", "" ], "text/plain": [ " Growth rate std. Lower 95% bound Upper 95% bound\n", "V \n", "e_z 60.0442 -5.5308e+01 175.3960\n", "z 102.1379 -3.7611e+02 580.3820\n", "k 0.0016 1.0269e-03 0.0022\n", "n 0.0099 9.3426e-03 0.0106\n", "i 0.2874 -3.1002e-01 0.8848\n", "y 0.0232 2.2066e-02 0.0244\n", "c 0.0003 8.1291e-05 0.0004\n", "rk 0.0239 2.2621e-02 0.0252\n", "w 0.0134 1.2714e-02 0.0141" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "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" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Error measures\n", "Marked text\n", "\n", "\n", "It is always important to get a handle on the accuracy of the solution. The `omega` function computes and aggregates the errors for the model's arbitrage section equations. For the RBC model these are the investment demand and labor supply equations. For each equation it reports the maximum error over the domain and the mean error using ergodic distribution weights (see the source for the [omega](https://github.com/EconForge/dolo/blob/master/dolo/algos/fg/accuracy.py) function)." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "ename": "ModuleNotFoundError", "evalue": "No module named 'dolo.algos.accuracy'", "output_type": "error", "traceback": [ "\u001b[0;31m----------------------------------------------------\u001b[0m", "\u001b[0;31mModuleNotFoundError\u001b[0mTraceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mdolo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malgos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccuracy\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0momega\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Perturbation solution\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0merr_pert\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0momega\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdr_pert\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0merr_pert\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'dolo.algos.accuracy'" ] } ], "source": [ "from dolo.algos.accuracy import omega\n", "\n", "print(\"Perturbation solution\")\n", "err_pert = omega(model, dr_pert)\n", "err_pert" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Global solution\n" ] }, { "ename": "NameError", "evalue": "name 'omega' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m----------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Global solution\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0merr_global\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0momega\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdr_global\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0merr_global\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'omega' is not defined" ] } ], "source": [ "print(\"Global solution\")\n", "err_global=omega(model, dr_global)\n", "err_global" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "breakpoint": false } }, "source": [ "The result of `omega` is a subclass of `dict`. `omega` fills that dict with some useful information that the default print does not reveal:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "err_pert.keys()" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "breakpoint": false } }, "source": [ "In particular the domain field contains information, like bounds and shape, that we can use to plot the spatial pattern of errors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "breakpoint": false } }, "outputs": [], "source": [ "a = err_pert['domain'].a\n", "b = err_pert['domain'].b\n", "orders = err_pert['domain'].orders\n", "errors = concatenate((err_pert['errors'].reshape( orders.tolist()+[-1] ),\n", " err_global['errors'].reshape( orders.tolist()+[-1] )),\n", " 2)\n", "\n", "figure(figsize=(8,6))\n", "\n", "titles=[\"Investment demand pertubation errors\",\n", " \"Labor supply pertubation errors\",\n", " \"Investment demand global errors\",\n", " \"Labor supply global errors\"]\n", "\n", "for i in range(4):\n", "\n", " subplot(2,2,i+1)\n", " imgplot = imshow(errors[:,:,i], origin='lower', \n", " extent=( a[0], b[0], a[1], b[1]), aspect='auto')\n", " imgplot.set_clim(0,3e-4)\n", " colorbar()\n", " xlabel('z')\n", " ylabel('k')\n", " title(titles[i])\n", "\n", "tight_layout()" ] } ], "metadata": { "jupytext": { "formats": "ipynb,py:light", "text_representation": { "extension": ".py", "format_name": "light", "format_version": "1.3", "jupytext_version": "0.8.3" } }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", 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