ipynb/barry_ke-greenwood-hercowitz-krusell.ipynb

{
    "cells": [
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Warning: Function psi has the same name as a MATLAB builtin. We suggest you rename the function to avoid a potential name conflict.\n",
                        "> In path (line 109)\n",
                        "  In addpath (line 86)\n",
                        "\n"
                    ]
                }
            ],
            "source": [
                "addpath('C:\\Users\\barry\\Documents\\Academics\\Quantecon-Notes\\functions')\n",
                "addpath('C:\\Users\\barry\\Documents\\Academics\\Quantecon-Notes\\functions\\CEtools')\n",
                "addpath('C:\\Users\\barry\\Documents\\Academics\\Quantecon-Notes\\functions\\CEtools\\public')\n",
                "cd 'C:\\Users\\barry\\Documents\\Academics\\Quantecon-Notes\\functions'"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Growth model with investment specific shock\n",
                "\n",
                "This notebook replicates a simplified model from *The role of investment-specific technological change in the business cycle (Greenwood, Hercowitz, and Krusell 1998)*"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "\n",
                "### Model setup\n",
                "Suppose instead of a standard optimal growth model, we focus on two different types of capital: structure and equipment. Each type of capital is subject to different growth process given investment and contribute to the output differently. Specifically, suppose the in-period utility of consumption is $u(c) = \\log(c)$. Denote the structure and equipment capital $k_s$ and $k_e$, respectively. The investments to structure and equipments are $i_s$ and $i_e$. (Here we ignore labor)\n",
                "\n",
                "The stochastic growth problem (in the form of Bellman equation) is \n",
                "$$\n",
                "V(k_s, k_e,q) = \\max_{c,i_s,i_e} \\frac{c^{1-\\eta}}{1-\\eta} + \\beta E_q[V(k_s', k_e',q')]\n",
                "$$\n",
                "subject to \n",
                "$$\n",
                "c+i_s+i_e = k_s^{\\alpha_s}k_e^{\\alpha_e}\\\\\n",
                "k_s' = (1-\\delta)k_s + i_s \\\\\n",
                "k_e' = (1-\\delta)k_e + e^q i_e \\\\\n",
                "c\\geq 0, i_s \\geq 0, i_e \\geq 0\n",
                "$$\n",
                "\n",
                "Using the first constraint we can replace $c$ in the Bellman equation\n",
                "$$\n",
                "V(k_s, k_e,q) = \\max_{c,i_s,i_e} \\frac{(k_s^{\\alpha_s}k_e^{\\alpha_e} - i_s - i_e)^{1-\\eta}}{1-\\eta} + \\beta E_q[V(k_s', k_e',q')]\n",
                "$$\n",
                "\n",
                "Following Greenwood-Hercowitz-Krusell, we set following parameters: $\\alpha_e = 0.18, \\alpha_s = 0.12, \\beta = 0.97, \\delta_s = 0.056, \\delta_e = 0.13, \\eta =2 $\n",
                "\n",
                ">In this economy, there is a shock to the efficiency of new capital produced (investment-specific technical change). A rise in $q$ is an improvement in the quality of $new$ capital goods because the same raw unit of investment goods have a larger impact on the capital stock used in production. In a competitive equilibrium, $q$ is the (log of the) price of consumption (and structure) $p_c$ relative to the price of equipment ($p_e$), i.e. $q_t = \\log(\\frac{p_{ct}}{p_{et}})$. As equipment quality improves relative to the (fixed) quality of consumption and structure, its relative price falls. "
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### 1. Parameterize the process of $e^q$ using Fred data"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 39,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        " \n",
                        "    ARIMA(1,0,0) Model (Gaussian Distribution):\n",
                        " \n",
                        "                   Value       StandardError    TStatistic      PValue   \n",
                        "                ___________    _____________    __________    ___________\n",
                        "\n",
                        "    Constant    -2.3954e-05     0.00074247      -0.032262         0.97426\n",
                        "    AR{1}           0.83727       0.032022         26.146     1.0854e-150\n",
                        "    Variance      0.0001131     6.4852e-06          17.44      4.0877e-68\n",
                        "\n",
                        "\n"
                    ]
                }
            ],
            "source": [
                "cd 'C:\\Users\\barry\\Documents\\Academics\\Quantecon-Notes'\n",
                "% first column is price index of personal nondurable consumption\n",
                "% second column is price index of private fixed investment\n",
                "data = csvread('technological_shock.csv',1,1); \n",
                "data(end,:) = [];\n",
                "\n",
                "\n",
                "% apply HP filter with smoothing parameter 1600 for quarterly data\n",
                "[T,C] = hpfilter(log(data(:,1) ./ data(:,2)), 1600);\n",
                "\n",
                "% Estimate an AR(1) process on the business cycle component\n",
                "mdl = arima(1,0,0);\n",
                "est_model = estimate(mdl,C);"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 86,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "\n"
                    ]
                }
            ],
            "source": [
                "% Approximate the AR(1) using 10 grids\n",
                "m = 10;\n",
                "[q, w] = qnwlogn(m,0,sigma^2/(1-rho^2));\n"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### 2. Solve the model\n",
                "\n",
                "We use the Compecon matlab package in Miranda and Fackler (2004)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 41,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "\n"
                    ]
                }
            ],
            "source": [
                "cd 'C:\\Users\\barry\\Documents\\Academics\\Quantecon-Notes\\functions'"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 118,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Created file 'C:\\Users\\barry\\Documents\\Academics\\Quantecon-Notes\\functions\\tech_shock.m'.\n"
                    ]
                }
            ],
            "source": [
                "%%file functions\\tech_shock.m\n",
                "function [out1, out2, out3] = tech_shock(flag,s,x,q,ae,as,delta_e,delta_s,eta)\n",
                "n = size(s,1);\n",
                "ds = 2;\n",
                "dx = 2;\n",
                "switch flag\n",
                "case 'b' %boundary condition of choice variable at different state point\n",
                "out1 = 0.0001*ones(size(s));\n",
                "out2 = [s(:,1).^(as).*s(:,2).^(ae) s(:,1).^(as).*s(:,2).^(ae)];\n",
                "case 'f'\n",
                "out1 = zeros(n,1);\n",
                "out2 = zeros(n,dx);\n",
                "out3 = zeros(n,dx,dx);\n",
                "out1 = (s(:,1).^as.*s(:,2).^ae - x(:,1) - x(:,2)).^(1-eta)/(1-eta);\n",
                "out2(:,1) = -(s(:,1).^as.*s(:,2).^ae - x(:,1) - x(:,2)).^(-eta);\n",
                "out2(:,2) = -(s(:,1).^as.*s(:,2).^ae - x(:,1) - x(:,2)).^(-eta);\n",
                "out3(:,1,1) = -eta.*(s(:,1).^as.*s(:,2).^ae - x(:,1) - x(:,2)).^(-eta-1);\n",
                "out3(:,2,1) = -eta.*(s(:,1).^as.*s(:,2).^ae - x(:,1) - x(:,2)).^(-eta-1);\n",
                "out3(:,1,2) = -eta.*(s(:,1).^as.*s(:,2).^ae - x(:,1) - x(:,2)).^(-eta-1);\n",
                "out3(:,2,2) = -eta.*(s(:,1).^as.*s(:,2).^ae - x(:,1) - x(:,2)).^(-eta-1);\n",
                "case 'g'\n",
                "out1 = zeros(n,ds);\n",
                "out2 = zeros(n,ds,dx);\n",
                "out3 = zeros(n,ds,dx,dx);\n",
                "out1 = [(1-delta_s).*s(:,1) + x(:,1) (1-delta_e).*s(:,2) + q.*x(:,2)];\n",
                "out2(:,1,1) = ones(n,1);\n",
                "out2(:,2,2) = q.*ones(n,1);\n",
                "end"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 142,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "\n"
                    ]
                }
            ],
            "source": [
                "ae = 0.18;\n",
                "as = 0.12;\n",
                "delta_e = 0.13;\n",
                "delta_s = 0.056;\n",
                "beta = 0.97;\n",
                "eta = 0.9;\n",
                "\n",
                "model.func = 'tech_shock';\n",
                "model.discount = beta;\n",
                "model.e = q;\n",
                "model.w = w;\n",
                "model.params = {ae as delta_e delta_s eta};\n",
                "\n",
                "n = [4 20];\n",
                "smin = [0.01 0.01];\n",
                "smax = [20 20];\n",
                "fspace = fundefn('spli',n,smin,smax);\n",
                "scoord = funnode(fspace);\n",
                "snodes = gridmake(scoord);\n",
                "\n",
                "qstar = 1;\n",
                "xinit = ones(size(snodes));\n",
                "vinit = 0.1.*zeros(size(snodes(:,1)));"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": []
        },
        {
            "cell_type": "code",
            "execution_count": 143,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "   1    1.5e+02\n",
                        "   2    1.2e+02\n",
                        "   3    2.1e-10\n",
                        "Elapsed Time =    6.35 Seconds\n",
                        "Warning: extrapolating beyond smax\n",
                        "\n"
                    ]
                }
            ],
            "source": [
                "qstar = 1;\n",
                "sstar = [(delta_s/(as*(delta_s*ae/(delta_e*as)))^ae)^(ae+as-1) (delta_e/(ae*(delta_e*as/(delta_s*ae)))^as)^(ae+as-1)];\n",
                "xstar = [sstar(1)*delta_s sstar(2)*delta_e];\n",
                "pstar = [0 0];\n",
                "[vlq,xlq,plq,ss,xx,pp] = lqapprox(model,snodes,sstar,xstar,pstar);\n",
                "[c,s,v,x] = dpsolve(model, fspace, snodes, vlq, xlq);"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 144,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "\n"
                    ]
                },
                {
                    "data": {
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\n",
                        "text/plain": [
                            "<IPython.core.display.Image object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "figure(1)\n",
                "hh = surf(s{1},s{2},x(:,:,1)');\n",
                "title('Optimal investment policy in structure')\n",
                "xlabel('Structure capital');\n",
                "ylabel('Equipment capital');\n",
                "zlabel('Structure investment')"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### 3. Simulation of expected capital growth"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 147,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "\n"
                    ]
                }
            ],
            "source": [
                "nyrs = 20;\n",
                "nrep = 1000;\n",
                "sinit = smin(ones(nrep,1),:);\n",
                "[spath,xpath] = dpsimul(model, sinit, nyrs, s,x);"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 151,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "\n"
                    ]
                },
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": [
                            "<IPython.core.display.Image object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "s1path = squeeze(spath(:,1,:));\n",
                "s2path = squeeze(spath(:,2,:));\n",
                "xpath = squeeze(xpath(:,1,:));\n",
                "\n",
                "figure(4)\n",
                "plot(0:nyrs, mean(s1path),0:nyrs, mean(s2path));\n",
                "title('Expected State Path');\n",
                "xlabel('Year');\n",
                "legend('Structure', 'Equipment')\n",
                "ylabel('Capital')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": []
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": []
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Matlab",
            "language": "matlab",
            "name": "matlab"
        },
        "language_info": {
            "codemirror_mode": "octave",
            "file_extension": ".m",
            "help_links": [
                {
                    "text": "MetaKernel Magics",
                    "url": "https://metakernel.readthedocs.io/en/latest/source/README.html"
                }
            ],
            "mimetype": "text/x-octave",
            "name": "matlab",
            "version": "0.16.7"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 2
}