ipynb/barry_ke-practicing_dynare_part_2.ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Practicing Dynare\n",
                "\n",
                "This notebook replicates the examples in *Practicing Dynare* by Barillas, Bhandari, Colacito, Kitao, Matthes, Sargent, and Shin. "
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2019-09-28T00:52:34.158891Z",
                    "start_time": "2019-09-28T00:51:46.478988Z"
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "\n"
                    ]
                }
            ],
            "source": [
                "addpath('C:\\dynare\\4.5.7\\matlab')\n",
                "base_path = 'C:\\Users\\barry\\Documents\\Academics\\Quantecon-Notes';\n",
                "cd 'C:\\Users\\barry\\Documents\\Academics\\Quantecon-Notes\\Dynare_model'"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Permanent Income Model\n",
                "\n",
                "Hall (1988)\n",
                "\n",
                "### Model\n",
                "\n",
                "A consumer faces the following problem\n",
                "$$\n",
                "\\max_{c_t, k_t} -\\frac{1}{2} \\sum_{t=0}^\\infty \\beta^t (c_t -b_t)^2\n",
                "$$\n",
                "subject to \n",
                "$$\n",
                "c_t + k_t = Rk_{t-1} +d_t\n",
                "$$\n",
                "$$\n",
                "d_t = \\rho d_{t-1} + (1-\\rho)\\mu_d + \\epsilon_d\n",
                "$$\n",
                "$$\n",
                "b_t = \\rho_b b_{t-1} + (1-\\rho_b)\\mu_b + \\epsilon_b\n",
                "$$\n",
                "Assume $\\beta R = 1$ and no depreciation, which leads to $i_t = k_t -k_{t-1}$\n",
                "The Euler equation requires $\\mu_{ct} = \\mu_{ct+1}, \\mu_{c_t} = b_t - c_t$ where $\\mu_{ct}$ is the marginal utility of consumption or Lagrange multiplier"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2019-09-28T01:05:00.938156Z",
                    "start_time": "2019-09-28T01:05:00.926222Z"
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Created file 'C:\\Users\\barry\\Documents\\Academics\\Quantecon-Notes\\Dynare_model\\permanent_income\\permanent_income.mod'.\n"
                    ]
                }
            ],
            "source": [
                "%%file Dynare_model\\permanent_income\\permanent_income.mod\n",
                "\n",
                "periods = 1000;\n",
                "var c k mu_c b d ian;\n",
                "varexo e_d e_b;\n",
                "\n",
                "parameters R rho rho_b mu_b mu_d;\n",
                "R = 1.05;\n",
                "\n",
                "rho = 0;\n",
                "mu_b = 30;\n",
                "mu_d = 5;\n",
                "rho_b = 0;\n",
                "\n",
                "model(linear);\n",
                "c+k = R*k(-1) + d;\n",
                "mu_c = b-c;\n",
                "mu_c = mu_c(+1);\n",
                "d = rho*d(-1)+mu_d*(1-rho)+e_d;\n",
                "b = (1-rho_b)*mu_b + rho_b*b(-1)+e_b;\n",
                "in = k - k(-1);\n",
                "end;\n",
                "\n",
                "initval;\n",
                "d= mu_d;\n",
                "k= 100;\n",
                "c = (R-1)*k+d;\n",
                "mu_c = mu_b-c;\n",
                "b = mu_b;\n",
                "end;\n",
                "\n",
                "shocks;\n",
                "var e_d;\n",
                "stderr 1;\n",
                "var e_b;\n",
                "stderr 1;\n",
                "end;\n",
                "\n",
                "steady;\n",
                "\n",
                "stoch_simul(dr_algo = 0, order = 1, periods = 1000, irf = 10);\n",
                "\n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2019-09-28T01:05:01.712133Z",
                    "start_time": "2019-09-28T01:05:01.693181Z"
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "\n"
                    ]
                }
            ],
            "source": [
                "cd 'C:\\Users\\barry\\Documents\\Academics\\Quantecon-Notes\\Dynare_model'\n",
                "cd 'permanent_income'"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2019-09-28T01:05:10.644500Z",
                    "start_time": "2019-09-28T01:05:02.228819Z"
                },
                "scrolled": true
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "\n",
                        "Configuring Dynare ...\n",
                        "[mex] Generalized QZ.\n",
                        "[mex] Sylvester equation solution.\n",
                        "[mex] Kronecker products.\n",
                        "[mex] Sparse kronecker products.\n",
                        "[mex] Local state space iteration (second order).\n",
                        "[mex] Bytecode evaluation.\n",
                        "[mex] k-order perturbation solver.\n",
                        "[mex] k-order solution simulation.\n",
                        "[mex] Quasi Monte-Carlo sequence (Sobol).\n",
                        "[mex] Markov Switching SBVAR.\n",
                        "\n",
                        "Using 64-bit preprocessor\n",
                        "Starting Dynare (version 4.5.7).\n",
                        "Starting preprocessing of the model file ...\n",
                        "WARNING: permanent_income.mod:2.1-15: periods: this command is now deprecated and may be removed in a future version of Dynare. Please use the ''periods'' option of the ''simul'' command instead.\n",
                        "WARNING: permanent_income.mod:40.13-23: dr_algo option is now deprecated, and may be removed in a future version of Dynare\n",
                        "Found 6 equation(s).\n",
                        "Evaluating expressions...done\n",
                        "Computing static model derivatives:\n",
                        " - order 1\n",
                        "Computing dynamic model derivatives:\n",
                        " - order 1\n",
                        " - order 2\n",
                        "Processing outputs ...\n",
                        "done\n",
                        "Preprocessing completed.\n",
                        "\n",
                        "\n",
                        "STEADY-STATE RESULTS:\n",
                        "\n",
                        "c    \t\t 10\n",
                        "k    \t\t 100\n",
                        "mu_c \t\t 20\n",
                        "b    \t\t 30\n",
                        "d    \t\t 5\n",
                        "in   \t\t 0\n",
                        "\n",
                        "MODEL SUMMARY\n",
                        "\n",
                        "  Number of variables:         6\n",
                        "  Number of stochastic shocks: 2\n",
                        "  Number of state variables:   3\n",
                        "  Number of jumpers:           1\n",
                        "  Number of static variables:  2\n",
                        "\n",
                        "\n",
                        "MATRIX OF COVARIANCE OF EXOGENOUS SHOCKS\n",
                        "Variables         e_d       e_b\n",
                        "e_d          1.000000  0.000000\n",
                        "e_b          0.000000  1.000000\n",
                        "\n",
                        "POLICY AND TRANSITION FUNCTIONS\n",
                        "                                   c               k            mu_c               b               d              in\n",
                        "Constant                   10.000000      100.000000       20.000000       30.000000        5.000000               0\n",
                        "k(-1)                       0.050000        1.000000       -0.050000               0               0               0\n",
                        "e_d                         0.047619        0.952381       -0.047619               0        1.000000        0.952381\n",
                        "e_b                         0.952381       -0.952381        0.047619        1.000000               0       -0.952381\n",
                        "\n",
                        "\n",
                        "MOMENTS OF SIMULATED VARIABLES\n",
                        "VARIABLE              MEAN       STD. DEV.        VARIANCE        SKEWNESS        KURTOSIS\n",
                        "c                11.211898        1.709717        2.923132       -0.065119       -0.713162\n",
                        "k               125.064032       27.710961      767.897387       -0.149162       -1.561661\n",
                        "mu_c             18.746798        1.385548        1.919743        0.149162       -1.561661\n",
                        "b                29.958697        0.973232        0.947180        0.028633        0.231899\n",
                        "d                 5.040443        1.002374        1.004753        0.157525        0.173943\n",
                        "in                0.077854        1.283084        1.646306        0.064787        0.024318\n",
                        "\n",
                        "\n",
                        "CORRELATION OF SIMULATED VARIABLES\n",
                        "VARIABLE         c       k    mu_c       b       d      in\n",
                        "c           1.0000  0.8223 -0.8223  0.5861  0.0694 -0.3718\n",
                        "k           0.8223  1.0000 -1.0000  0.0208  0.0363  0.0120\n",
                        "mu_c       -0.8223 -1.0000  1.0000 -0.0208 -0.0363 -0.0120\n",
                        "b           0.5861  0.0208 -0.0208  1.0000  0.0702 -0.6702\n",
                        "d           0.0694  0.0363 -0.0363  0.0702  1.0000  0.6933\n",
                        "in         -0.3718  0.0120 -0.0120 -0.6702  0.6933  1.0000\n",
                        "\n",
                        "\n",
                        "AUTOCORRELATION OF SIMULATED VARIABLES\n",
                        "VARIABLE         1       2       3       4       5\n",
                        "c           0.6914  0.6693  0.7053  0.6969  0.6797\n",
                        "k           0.9978  0.9967  0.9958  0.9947  0.9937\n",
                        "mu_c        0.9978  0.9967  0.9958  0.9947  0.9937\n",
                        "b           0.0120 -0.0592  0.0598  0.0311 -0.0221\n",
                        "d           0.0381 -0.0438  0.0052 -0.0012  0.0236\n",
                        "in          0.0080 -0.0661  0.0393 -0.0332  0.0018\n",
                        "\n",
                        "\n",
                        "\n",
                        "VARIANCE DECOMPOSITION SIMULATING ONE SHOCK AT A TIME (in percent)\n",
                        "                     e_d               e_b  Tot. lin. contr.\n",
                        "c                  27.11             46.65             73.76\n",
                        "k                  41.28             21.99             63.27\n",
                        "mu_c               41.28             21.99             63.27\n",
                        "b                   0.00            100.11            100.11\n",
                        "d                 100.11              0.00            100.11\n",
                        "in                 55.42             52.24            107.66\n",
                        "Note: numbers do not add up to 100 due to non-zero correlation of simulated shocks in small samples\n",
                        "\n",
                        "Total computing time : 0h00m06s\n",
                        "Note: 2 warning(s) encountered in the preprocessor\n",
                        "\n"
                    ]
                },
                {
                    "data": {
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\n",
                        "text/plain": [
                            "<IPython.core.display.Image object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": [
                            "<IPython.core.display.Image object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "dynare permanent_income.mod"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Labor market for engineers\n",
                "\n",
                "Ryoo and Rosen (2004)\n",
                "\n",
                "### Model\n",
                "Number of engineers: $N_t$. Number of new enterants into engineering school $s_t$. Wage level $w_t$. It takes $k$ periods of schooling to become an engineer. \n",
                "\n",
                "The present value of the engineer is \n",
                "$$\n",
                "P_t = (1-\\delta)\\beta P_{t+1} + (1-\\delta)^k \\beta^k w_{t+k}\n",
                "$$\n",
                "The supply of new engineering students \n",
                "$$\n",
                "s_t = a_0 + a_1 P_t + \\epsilon_t\n",
                "$$\n",
                "A time-to-build structure of education process\n",
                "$$\n",
                "N_t = (1-\\delta)N_{t-1} + s_{t-k}\n",
                "$$\n",
                "A demand curve for engineers \n",
                "$$\n",
                "N_t = d_0 - d_1 w_t + e_{dt}\n",
                "$$"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 15,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2019-09-28T01:13:21.838551Z",
                    "start_time": "2019-09-28T01:13:21.822595Z"
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Created file 'C:\\Users\\barry\\Documents\\Academics\\Quantecon-Notes\\Dynare_model\\engineer_market\\engineer_market.mod'.\n"
                    ]
                }
            ],
            "source": [
                "%%file Dynare_model\\engineer_market\\engineer_market.mod\n",
                "\n",
                "periods 500;\n",
                "var s N P W;\n",
                "varexo e_s e_d;\n",
                "parameters a0 a1 delta d0 d1 bet k;\n",
                "a0=10;\n",
                "a1=1;\n",
                "d0=1000;\n",
                "d1=1;\n",
                "bet=.99;\n",
                "delta=.02;\n",
                "model(linear);\n",
                "s=a0+a1*P+e_s; // flow supply of new entrants\n",
                "N=(1-delta)*N(-1) + s(-4); // evolution of the stock\n",
                "N=d0-d1*W+e_d; // stock demand equation\n",
                "P=bet*(1-delta)*P(+1)+bet^4*(1-delta)^4*W(+4); // present value of\n",
                "//wages\n",
                "\n",
                "end;\n",
                "initval;\n",
                "s=0;\n",
                "N=0;\n",
                "P=0;\n",
                "W=0;\n",
                "end;\n",
                "shocks;\n",
                "var e_d;\n",
                "stderr 1;\n",
                "var e_s;\n",
                "stderr 1;\n",
                "end;\n",
                "steady;\n",
                "check;\n",
                "stoch_simul(dr_algo=0, order=1, periods=500, irf=10);"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2019-09-28T01:13:22.948279Z",
                    "start_time": "2019-09-28T01:13:22.925340Z"
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "\n"
                    ]
                }
            ],
            "source": [
                "cd 'C:\\Users\\barry\\Documents\\Academics\\Quantecon-Notes\\Dynare_model'\n",
                "cd 'engineer_market'"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 17,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2019-09-28T01:13:26.648479Z",
                    "start_time": "2019-09-28T01:13:23.509657Z"
                },
                "scrolled": false
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "\n",
                        "Configuring Dynare ...\n",
                        "[mex] Generalized QZ.\n",
                        "[mex] Sylvester equation solution.\n",
                        "[mex] Kronecker products.\n",
                        "[mex] Sparse kronecker products.\n",
                        "[mex] Local state space iteration (second order).\n",
                        "[mex] Bytecode evaluation.\n",
                        "[mex] k-order perturbation solver.\n",
                        "[mex] k-order solution simulation.\n",
                        "[mex] Quasi Monte-Carlo sequence (Sobol).\n",
                        "[mex] Markov Switching SBVAR.\n",
                        "\n",
                        "Using 64-bit preprocessor\n",
                        "Starting Dynare (version 4.5.7).\n",
                        "Starting preprocessing of the model file ...\n",
                        "WARNING: engineer_market.mod:2.1-12: periods: this command is now deprecated and may be removed in a future version of Dynare. Please use the ''periods'' option of the ''simul'' command instead.\n",
                        "WARNING: engineer_market.mod:34.13-21: dr_algo option is now deprecated, and may be removed in a future version of Dynare\n",
                        "Substitution of endo leads >= 2: added 3 auxiliary variables and equations.\n",
                        "Substitution of endo lags >= 2: added 3 auxiliary variables and equations.\n",
                        "Found 10 equation(s).\n",
                        "Evaluating expressions...done\n",
                        "Computing static model derivatives:\n",
                        " - order 1\n",
                        "Computing dynamic model derivatives:\n",
                        " - order 1\n",
                        " - order 2\n",
                        "Processing outputs ...\n",
                        "done\n",
                        "Preprocessing completed.\n",
                        "\n",
                        "Warning: Some of the parameters have no value (k) when using steady. If these parameters are not initialized in a steadystate file or a steady_state_model-block, Dynare may not be able to solve the model...\n",
                        "\n",
                        "STEADY-STATE RESULTS:\n",
                        "\n",
                        "s                \t\t 19.9933\n",
                        "N                \t\t 999.664\n",
                        "P                \t\t 9.99328\n",
                        "W                \t\t 0.336108\n",
                        "\n",
                        "EIGENVALUES:\n",
                        "         Modulus             Real        Imaginary\n",
                        "\n",
                        "        1.87e-05         1.87e-05                0\n",
                        "       1.899e-05        2.827e-07        1.899e-05\n",
                        "       1.899e-05        2.827e-07       -1.899e-05\n",
                        "       1.927e-05       -1.927e-05                0\n",
                        "          0.4002           0.4002                0\n",
                        "           2.524            2.524                0\n",
                        "             Inf              Inf                0\n",
                        "             Inf              Inf                0\n",
                        "             Inf              Inf                0\n",
                        "             Inf              Inf                0\n",
                        "\n",
                        "\n",
                        "There are 5 eigenvalue(s) larger than 1 in modulus \n",
                        "for 5 forward-looking variable(s)\n",
                        "\n",
                        "The rank condition is verified.\n",
                        "\n",
                        "Warning: Some of the parameters have no value (k) when using stoch_simul. If these parameters are not initialized in a steadystate file or a steady_state_model-block, Dynare may not be able to solve the model...\n",
                        "\n",
                        "MODEL SUMMARY\n",
                        "\n",
                        "  Number of variables:         10\n",
                        "  Number of stochastic shocks: 2\n",
                        "  Number of state variables:   5\n",
                        "  Number of jumpers:           5\n",
                        "  Number of static variables:  0\n",
                        "\n",
                        "\n",
                        "MATRIX OF COVARIANCE OF EXOGENOUS SHOCKS\n",
                        "Variables         e_s       e_d\n",
                        "e_s          1.000000  0.000000\n",
                        "e_d          0.000000  1.000000\n",
                        "\n",
                        "POLICY AND TRANSITION FUNCTIONS\n",
                        "                                   s               N               P               W\n",
                        "Constant                   19.993278      999.663892        9.993278        0.336108\n",
                        "s(-1)                      -0.579755               0       -0.579755               0\n",
                        "N(-1)                      -0.534748        0.980000       -0.534748       -0.980000\n",
                        "s(-2)                      -0.568160               0       -0.568160               0\n",
                        "s(-3)                      -0.556797               0       -0.556797               0\n",
                        "s(-4)                      -0.545661        1.000000       -0.545661       -1.000000\n",
                        "e_s                         0.408413               0       -0.591587               0\n",
                        "e_d                                0               0               0        1.000000\n",
                        "\n",
                        "\n",
                        "MOMENTS OF SIMULATED VARIABLES\n",
                        "VARIABLE              MEAN       STD. DEV.        VARIANCE        SKEWNESS        KURTOSIS\n",
                        "s                19.994406        0.467210        0.218285        0.070188        0.271218\n",
                        "N               999.659865        0.442565        0.195864        0.077781       -0.004597\n",
                        "P                 9.994158        0.644203        0.414997       -0.069889       -0.037787\n",
                        "W                 0.245707        1.089042        1.186012        0.003848       -0.003332\n",
                        "\n",
                        "\n",
                        "CORRELATION OF SIMULATED VARIABLES\n",
                        "VARIABLE         s       N       P       W\n",
                        "s           1.0000 -0.0660 -0.5462  0.0986\n",
                        "N          -0.0660  1.0000  0.0059 -0.4641\n",
                        "P          -0.5462  0.0059  1.0000 -0.0546\n",
                        "W           0.0986 -0.4641 -0.0546  1.0000\n",
                        "\n",
                        "\n",
                        "AUTOCORRELATION OF SIMULATED VARIABLES\n",
                        "VARIABLE         1       2       3       4       5\n",
                        "s          -0.2479 -0.1763 -0.0114 -0.0176 -0.0977\n",
                        "N           0.4322  0.1494  0.0677 -0.0112 -0.0684\n",
                        "P           0.4336  0.1499  0.0624 -0.0077 -0.0545\n",
                        "W           0.1335 -0.0101  0.0258 -0.0561 -0.0737\n",
                        "\n",
                        "\n",
                        "\n",
                        "VARIANCE DECOMPOSITION SIMULATING ONE SHOCK AT A TIME (in percent)\n",
                        "                  e_s               e_d  Tot. lin. contr.\n",
                        "s              100.25              0.00            100.25\n",
                        "N              100.25              0.00            100.25\n",
                        "P              100.25              0.00            100.25\n",
                        "W               16.56             78.99             95.54\n",
                        "Note: numbers do not add up to 100 due to non-zero correlation of simulated shocks in small samples\n",
                        "\n",
                        "Total computing time : 0h00m02s\n",
                        "Note: 2 warning(s) encountered in the preprocessor\n",
                        "Note: warning(s) encountered in MATLAB/Octave code\n",
                        "\n"
                    ]
                },
                {
                    "data": {
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\n",
                        "text/plain": [
                            "<IPython.core.display.Image object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": [
                            "<IPython.core.display.Image object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "dynare engineer_market.mod"
            ]
        },
        {
            "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"
        },
        "latex_envs": {
            "LaTeX_envs_menu_present": true,
            "autoclose": false,
            "autocomplete": true,
            "bibliofile": "biblio.bib",
            "cite_by": "apalike",
            "current_citInitial": 1,
            "eqLabelWithNumbers": true,
            "eqNumInitial": 1,
            "hotkeys": {
                "equation": "Ctrl-E",
                "itemize": "Ctrl-I"
            },
            "labels_anchors": false,
            "latex_user_defs": false,
            "report_style_numbering": false,
            "user_envs_cfg": false
        },
        "varInspector": {
            "cols": {
                "lenName": 16,
                "lenType": 16,
                "lenVar": 40
            },
            "kernels_config": {
                "python": {
                    "delete_cmd_postfix": "",
                    "delete_cmd_prefix": "del ",
                    "library": "var_list.py",
                    "varRefreshCmd": "print(var_dic_list())"
                },
                "r": {
                    "delete_cmd_postfix": ") ",
                    "delete_cmd_prefix": "rm(",
                    "library": "var_list.r",
                    "varRefreshCmd": "cat(var_dic_list()) "
                }
            },
            "types_to_exclude": [
                "module",
                "function",
                "builtin_function_or_method",
                "instance",
                "_Feature"
            ],
            "window_display": false
        }
    },
    "nbformat": 4,
    "nbformat_minor": 2
}