Coding problem

profile9qh0o1k52
dolo_rbc_linear_4_9_9_irf.ipynb

{ "cells": [ { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " Model:\n", " ------\n", " name: \"Real Business Cycle\"\n", " type: \"dtcc\"\n", " file: \"rby_4_9_9.yaml\n", "\n", "Equations:\n", "----------\n", "\n", "transition\n", " 1 : 0.0000 : z(0) == ((1) - (rho)) * (zbar) + (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 : (1) - (((beta) * (((c(0)) / (c(1))) ** (sigma))) * ((1) - (delta) + rk(1)))\n", " 2 : \u001b[31m-1.8947\u001b[0m : (((chi) * ((n(0)) ** (eta))) * ((c(0)) ** (sigma))) - (w(0))\n", "\n", "definitions\n", " 1 : y = 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" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x288 with 6 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "from dolo import *\n", "model = yaml_import(\"rby_4_9_9.yaml\")\n", "#dr = approximate_controls(model, order=1)\n", "dr = perturbate(model)\n", "#dr_global = time_iteration(model, pert_order=1, smolyak_order=3)\n", "\n", "#Old DOLO (4.9.5)\n", "#sim = simulate(model, dr, s0, n=100, horizon=40 )\n", "#New DOLO\n", "\n", "#sim = simulate(model, dr, N=10, T=40 )\n", "irf = response(model,dr, 'e_z')\n", "\n", "print(model)\n", "\n", "\n", "#plt.plot([1,2,3])\n", "\n", "\n", "plt.figure(figsize=(8,4))\n", "plt.subplot(231)\n", "plt.plot(irf.sel(V='z'))\n", "plt.title('Productivity')\n", "plt.grid()\n", "plt.subplot(232)\n", "plt.plot(irf.sel(V='i'))\n", "plt.title('Investment')\n", "plt.grid()\n", "plt.subplot(233)\n", "plt.plot(irf.sel(V='n'))\n", "plt.grid()\n", "plt.title('Labour')\n", "plt.subplot(234)\n", "plt.plot(irf.sel(V='c'))\n", "plt.title('Consumption')\n", "plt.grid()\n", "plt.tight_layout()\n", "plt.subplot(235)\n", "plt.plot(irf.sel(V='y'))\n", "plt.title('Output')\n", "plt.grid()\n", "plt.tight_layout()\n", "plt.subplot(236)\n", "plt.plot(irf.sel(V='k'))\n", "plt.title('Capital')\n", "plt.grid()\n", "plt.tight_layout()\n", "\n", " \n", " #hold\n", "\n", "#plt.subplot(212)\n", "#plt.plot(sim.iloc[i, :, i_i], color='blue', alpha=0.1)\n", "plt.show( )\n", "\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.33" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.calibration['i']\n", "model.calibration['z']\n", "model.calibration['k']\n", "model.calibration['y']\n", "model.calibration['c']\n", "model.calibration['n']" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<xarray.DataArray (T: 40)>\n", "array([ 0. , 0.010363, 0.002807, 0.019574, 0.027616, 0.022177,\n", " -0.004636, 0.010205, -0.003105, -0.000946, -0.000297, 0.000716,\n", " 0.033987, 0.029146, 0.020518, 0.035386, 0.015217, 0.001838,\n", " 0.011626, -0.002034, 0.017143, 0.001036, -0.013086, 0.001047,\n", " -0.005306, -0.012991, -0.015694, -0.022721, 0.014019, 0.016812,\n", " 0.004333, -0.003152, 0.02387 , 0.022653, 0.021588, 0.020398,\n", " 0.017042, 0.046431, 0.022484, -0.00149 ])\n", "Coordinates:\n", " * T (T) int32 0 1 2 3 4 5 6 7 8 9 10 ... 30 31 32 33 34 35 36 37 38 39\n", " N int32 2\n", " V <U3 'z'" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 }