{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sudden Stop Model\n", "\n", "In this notebook we replicate the baseline model exposed in \n", "\n", "`From Sudden Stops to Fisherian Deflation, Quantitative Theory and Policy` by __Anton Korinek and Enrique G. Mendoza__\n", "\n", "The file `sudden_stop.yaml` which is printed below, describes the model, and must be included in the same directory as this notebook." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## importing necessary functions" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from dolo import *\n", "from dolo.algos import time_iteration\n", "from dolo.algos import plot_decision_rule, simulate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## writing the model" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'/home/pablo/Mobilhome/econforge/dolo/examples/notebooks'" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pwd" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "\n", " 
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# This file adapts the model described in\n",
       "# "From Sudden Stops to Fisherian Deflation, Quantitative Theory and Policy"\n",
       "# by Anton Korinek and Enrique G. Mendoza\n",
       "\n",
       "name: Sudden Stop (General)\n",
       "\n",
       "symbols:\n",
       "\n",
       "    exogenous: [y]\n",
       "    states: [l]\n",
       "    controls: [b, lam]\n",
       "    values: [V, Vc]\n",
       "    parameters: [beta, R, sigma, a, mu, kappa, delta_y, pi, lam_inf]\n",
       "\n",
       "\n",
       "definitions:\n",
       "    c: 1 + y + l*R - b\n",
       "\n",
       "equations:\n",
       "\n",
       "    transition:\n",
       "        - l = b(-1)\n",
       "\n",
       "    arbitrage:\n",
       "        - lam = b/c\n",
       "        - 1 - beta*(c(1)/c)^(-sigma)*R    |  lam_inf <= lam <= inf\n",
       "\n",
       "    value:\n",
       "        - V = c^(1.0-sigma)/(1.0-sigma) + beta*V(1)\n",
       "        - Vc = c^(1.0-sigma)/(1.0-sigma)\n",
       "\n",
       "calibration:\n",
       "\n",
       "    beta: 0.95\n",
       "    R: 1.03\n",
       "    sigma: 2.0\n",
       "    a: 1/3\n",
       "    mu: 0.8\n",
       "    kappa: 1.3\n",
       "    delta_y: 0.03\n",
       "    pi: 0.05\n",
       "    lam_inf: -0.2\n",
       "    y: 1.0\n",
       "    c: 1.0 + y\n",
       "    b: 0.0\n",
       "    l: 0.0\n",
       "    lam: 0.0\n",
       "\n",
       "    V: c^(1.0-sigma)/(1.0-sigma)/(1.0-beta)\n",
       "    Vc: c^(1.0-sigma)/(1.0-sigma)\n",
       "\n",
       "\n",
       "exogenous: !MarkovChain\n",
       "    values: [[ 1.0-delta_y ],  # bad state\n",
       "             [ 1.0 ]]          # good state\n",
       "    transitions: [[ 0.5, 1-0.5 ],   # probabilities   [p(L|L), p(H|L)]\n",
       "                  [ 0.5, 0.5 ]]     # probabilities   [p(L|H), p(H|H)]\n",
       "\n",
       "options:\n",
       "    grid: !Cartesian\n",
       "        a: [-1.0]\n",
       "        b: [ 1.0]\n",
       "        orders: [10]\n",
       "
\n", "
\n", " " ], "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# filename = 'https://raw.githubusercontent.com/EconForge/dolo/master/examples/models/compat/sudden_stop.yaml'\n", "filename = '../models/sudden_stop.yaml'\n", "# the model file is coded in a separate file called sudden_stop.yaml\n", "# note how the borrowing constraint is implemented as complementarity condition\n", "pcat(filename)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## importing the model\n", "\n", "Note, that residuals, are not zero at the calibration we supply. This is because the representative agent is impatient\n", "and we have $\\beta<1/R$. In this case it doesn't matter.\n", "\n", "By default, the calibrated value for endogenous variables are used as a (constant) starting point for the decision rules." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Model
nameSudden Stop (General)
typedtcc
filename../models/sudden_stop.yaml
\n", "\n", "\n", "
TypeEquationResidual
transition$l_{t} = b_{t-1}$0.000
arbitrage$\\lambda_{t} = \\frac{b_{t}}{c_{t}}$0.000
$1 - \\beta \\; \\left(\\frac{c_{t+1}}{c_{t}}\\right)^{- \\sigma} \\; R$0.0215
value$V_{t} = \\frac{c_{t}^{1.0 - \\sigma}}{1.0 - \\sigma} + \\beta \\; V_{t+1}$
$Vc_{t} = \\frac{c_{t}^{1.0 - \\sigma}}{1.0 - \\sigma}$
definitions$c_{t} = 1 + y_{t} + l_{t} \\; R - b_{t}$
" ], "text/plain": [ "\n", " Model:\n", " ------\n", " name: \"Sudden Stop (General)\"\n", " type: \"dtcc\"\n", " file: \"../models/sudden_stop.yaml\n", "\n", "Equations:\n", "----------\n", "\n", "transition\n", " 1 : 0.0000 : l = b(-1)\n", "\n", "arbitrage\n", " 1 : 0.0000 : lam = b/c\n", " 2 : \u001b[31m0.0215\u001b[0m : 1 - beta*(c(1)/c)**(-sigma)*R | lam_inf <= lam <= inf\n", "\n", "value\n", " 1 : 0.0000 : V = c**(1.0-sigma)/(1.0-sigma) + beta*V(1)\n", " 2 : 0.0000 : Vc = c**(1.0-sigma)/(1.0-sigma)\n", "\n", "definitions\n", " 1 : c = 1 + y + l*R - b\n" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = yaml_import(filename)\n", "model" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "get_grid() got an unexpected keyword argument 'orders'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\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[0;31m# to avoid numerical glitches we choose a relatively high number of grid points\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmdr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime_iteration\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'orders'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/home/pablo/Mobilhome/econforge/dolo/dolo/algos/time_iteration.py\u001b[0m in \u001b[0;36mtime_iteration\u001b[0;34m(model, initial_guess, with_complementarities, verbose, grid, maxit, inner_maxit, tol, hook)\u001b[0m\n\u001b[1;32m 76\u001b[0m \u001b[0mn_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msymbols\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'states'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 77\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 78\u001b[0;31m \u001b[0mendo_grid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_grid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 79\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 80\u001b[0m \u001b[0minterp_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'cubic'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mTypeError\u001b[0m: get_grid() got an unexpected keyword argument 'orders'" ] } ], "source": [ "# to avoid numerical glitches we choose a relatively high number of grid points\n", "mdr = time_iteration(model, verbose=True, grid={'orders':[1000]})" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'mdr' 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 4\u001b[0m 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\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mplot_decision_rule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmdr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'l'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'b'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi0\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_steps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'$b_t$ (good state)'\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mplot_decision_rule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmdr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'l'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'l'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi0\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_steps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinestyle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'--'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'black'\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'mdr' is not defined" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# produce the plots\n", "n_steps = 100\n", "\n", "figure(figsize(10,6))\n", "subplot(121)\n", "plot_decision_rule(model, mdr, 'l', 'b', i0=0, n_steps=n_steps, label='$b_t$ (bad state)' )\n", "plot_decision_rule(model, mdr, 'l', 'b', i0=1, n_steps=n_steps, label='$b_t$ (good state)' )\n", "plot_decision_rule(model, mdr, 'l', 'l', i0=1, n_steps=n_steps, linestyle='--', color='black' )\n", "#plot(df['l'], df['l'], linestyle='--', color='black')\n", "\n", "# to plot the borrowing limit, we produce a dataframe df which contains all series\n", "# (note that we don't supply a variable name to plot, only the state 'l')\n", "\n", "lam_inf = model.get_calibration('lam_inf')\n", "df = plot_decision_rule(model, mdr, 'l', i0=0, n_steps=n_steps)\n", "plot(df['l'], lam_inf*df['c'], linestyle='--', color='black')\n", "\n", "xlabel('$l_t$')\n", "\n", "legend(loc= 'upper left')\n", "\n", "\n", "subplot(122)\n", "plot_decision_rule(model, mdr, 'l', 'c', i0=0, n_steps=n_steps, label='$c_t$ (bad state)' )\n", "plot_decision_rule(model, mdr, 'l', 'c', i0=1, n_steps=n_steps, label='$c_t$ (good state)' )\n", "legend(loc= 'lower right')\n", "xlabel('$l_t$')\n", "\n", "suptitle(\"Decision Rules\")\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "## stochastic simulations" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "i_0 = 1 # we start from the good state\n", "sim = simulate(model, mdr, i_0, s0=0.5, n_exp=1, horizon=100) # markov_indices=markov_indices)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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hgr+03x1Z5Dn1XgpmSdZ7s+sniiyj6GsnstrWcZcvi3CQts5J33SS3n3VSFmP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\n7rsPLrwQfvpTsI4vyxMRERFpTBf/J3DmmbBgAfz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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "subplot(211)\n", "plot(sim['y'])\n", "subplot(212)\n", "plot(sim['b'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sensitivity analysis\n", "\n", "Here we want to compare the saving behaviour as a function of risk aversion $\\sigma$.\n", "We contrast the baseline $\\sigma=2$ with the high aversion scenario $\\sigma=16$." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Solving WITH complementarities.\n", "------------------------------------------------\n", "| N | Error | Gain | Time | nit |\n", "------------------------------------------------\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[33mUserWarning\u001b[0m:/home/pablo/Programming/econforge/dolo/dolo/numeric/optimize/newton.py:150\n", " Did not converge\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "| 1 | 5.133e-01 | nan | 0.475 | 10 |\n", "| 2 | 1.703e-01 | 0.332 | 0.744 | 8 |\n", "| 3 | 8.435e-02 | 0.495 | 0.369 | 7 |\n", "| 4 | 5.005e-02 | 0.593 | 0.406 | 7 |\n", "| 5 | 3.292e-02 | 0.658 | 0.405 | 7 |\n", "| 6 | 2.313e-02 | 0.703 | 0.390 | 7 |\n", "| 7 | 1.702e-02 | 0.736 | 0.464 | 7 |\n", "| 8 | 1.295e-02 | 0.761 | 0.362 | 7 |\n", "| 9 | 1.011e-02 | 0.780 | 0.376 | 7 |\n", "| 10 | 8.045e-03 | 0.796 | 0.372 | 7 |\n", "| 11 | 6.501e-03 | 0.808 | 0.326 | 7 |\n", "| 12 | 5.316e-03 | 0.818 | 0.425 | 7 |\n", "| 13 | 4.387e-03 | 0.825 | 0.384 | 6 |\n", "| 14 | 3.647e-03 | 0.831 | 0.386 | 7 |\n", "| 15 | 3.048e-03 | 0.836 | 0.362 | 7 |\n", "| 16 | 2.558e-03 | 0.839 | 0.385 | 6 |\n", "| 17 | 2.206e-03 | 0.863 | 0.318 | 6 |\n", "| 18 | 2.010e-03 | 0.911 | 0.489 | 6 |\n", "| 19 | 1.842e-03 | 0.916 | 0.243 | 5 |\n", "| 20 | 1.699e-03 | 0.922 | 0.305 | 5 |\n", "| 21 | 1.580e-03 | 0.930 | 0.280 | 5 |\n", "| 22 | 1.472e-03 | 0.932 | 0.240 | 5 |\n", "| 23 | 1.374e-03 | 0.933 | 0.271 | 5 |\n", "| 24 | 1.289e-03 | 0.938 | 0.295 | 5 |\n", "| 25 | 1.210e-03 | 0.939 | 0.454 | 5 |\n", "| 26 | 1.137e-03 | 0.940 | 0.273 | 5 |\n", "| 27 | 1.073e-03 | 0.944 | 0.189 | 4 |\n", "| 28 | 1.013e-03 | 0.944 | 0.234 | 4 |\n", "| 29 | 9.575e-04 | 0.945 | 0.271 | 3 |\n", "| 30 | 9.075e-04 | 0.948 | 0.208 | 3 |\n", "| 31 | 8.600e-04 | 0.948 | 0.136 | 3 |\n", "| 32 | 8.166e-04 | 0.950 | 0.149 | 3 |\n", "| 33 | 7.764e-04 | 0.951 | 0.141 | 3 |\n", "| 34 | 7.384e-04 | 0.951 | 0.285 | 3 |\n", "| 35 | 7.035e-04 | 0.953 | 0.284 | 3 |\n", "| 36 | 6.705e-04 | 0.953 | 0.093 | 2 |\n", "| 37 | 6.396e-04 | 0.954 | 0.091 | 2 |\n", "| 38 | 6.108e-04 | 0.955 | 0.275 | 2 |\n", "| 39 | 5.835e-04 | 0.955 | 0.099 | 2 |\n", "| 40 | 5.579e-04 | 0.956 | 0.096 | 2 |\n", "| 41 | 5.338e-04 | 0.957 | 0.111 | 2 |\n", "| 42 | 5.110e-04 | 0.957 | 0.103 | 2 |\n", "| 43 | 4.895e-04 | 0.958 | 0.093 | 2 |\n", "| 44 | 4.691e-04 | 0.958 | 0.094 | 2 |\n", "| 45 | 4.499e-04 | 0.959 | 0.167 | 2 |\n", "| 46 | 4.316e-04 | 0.959 | 0.124 | 2 |\n", "| 47 | 4.143e-04 | 0.960 | 0.119 | 2 |\n", "| 48 | 3.978e-04 | 0.960 | 0.104 | 2 |\n", "| 49 | 3.821e-04 | 0.961 | 0.117 | 2 |\n", "| 50 | 3.598e-04 | 0.941 | 0.096 | 2 |\n", "| 51 | 3.132e-04 | 0.871 | 0.128 | 2 |\n", "| 52 | 2.476e-04 | 0.790 | 0.141 | 2 |\n", "| 53 | 1.782e-04 | 0.720 | 0.126 | 2 |\n", "| 54 | 1.190e-04 | 0.668 | 0.112 | 2 |\n", "| 55 | 7.541e-05 | 0.634 | 0.273 | 2 |\n", "| 56 | 4.634e-05 | 0.615 | 0.096 | 2 |\n", "| 57 | 2.802e-05 | 0.605 | 0.096 | 2 |\n", "| 58 | 1.684e-05 | 0.601 | 0.093 | 2 |\n", "| 59 | 1.010e-05 | 0.600 | 0.051 | 1 |\n", "| 60 | 6.072e-06 | 0.601 | 0.051 | 1 |\n", "| 61 | 3.659e-06 | 0.603 | 0.084 | 1 |\n", "| 62 | 2.211e-06 | 0.604 | 0.093 | 1 |\n", "| 63 | 1.340e-06 | 0.606 | 0.108 | 1 |\n", "| 64 | 8.141e-07 | 0.607 | 0.071 | 1 |\n", "------------------------------------------------\n", "Elapsed: 15.010632991790771 seconds.\n", "------------------------------------------------\n" ] } ], "source": [ "# we solve the model with sigma=16\n", "model.set_calibration(sigma=16.0)\n", "mdr_high_gamma = time_iteration(model, verbose=True, grid={'orders':[1000]})" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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RUS1YsEClp6er9evXK0tLS5NrlVJq2LBhatSoUTn2R6fTqejo6Gz98fb2Vleu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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# now we compare the decision rules with low and high risk aversion\n", "plot_decision_rule(model, mdr, 'l', 'b', i0=0, n_steps=n_steps, label='$b_t$ (bad)' )\n", "plot_decision_rule(model, mdr, 'l', 'b', i0=1, n_steps=n_steps, label='$b_t$ (good)' )\n", "plot_decision_rule(model, mdr_high_gamma, 'l', 'b', i0=0, n_steps=n_steps, label='$b_t$ (bad) [high gamma]' )\n", "plot_decision_rule(model, mdr_high_gamma, 'l', 'b', i0=1, n_steps=n_steps, label='$b_t$ (good) [high gamma]' )\n", "plot(df['l'], df['l'], linestyle='--', color='black')\n", "plot(df['l'], -0.2*df['c'], linestyle='--', color='black')\n", "legend(loc= 'upper left')" ] } ], "metadata": { "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", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 1 }