ipynb/barry_ke-practicing_dynare_part1.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": 4,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2019-09-23T00:28:53.089590Z",
                    "start_time": "2019-09-23T00:28:52.996838Z"
                }
            },
            "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": [
                "## Neoclassical Growth Model\n",
                "Cooley and Prescott 1995\n",
                "\n",
                "### Model\n",
                "\n",
                "A representative agent's problem is \n",
                "$$\n",
                "\\max_{c_t,l_t} E_0 \\sum_{t=1}^\\infty \\beta^{t-1} \\frac{c_t^\\theta (1-l_t)^{1-\\theta})^{1-\\tau}}{1-\\tau}\n",
                "$$\n",
                "\n",
                "subject to the resource constraint\n",
                "$$\n",
                "c_t + i_t = e^{z_t}k_t^\\alpha l_t^{1-\\alpha}\n",
                "$$\n",
                "the motion of capital\n",
                "$$\n",
                "k_{t+1} = i_t + (1-\\delta)k_t\n",
                "$$\n",
                "and stochastic process for productivity \n",
                "$$\n",
                "z_t = \\rho z_{t-1} + s\\epsilon_t\n",
                "$$\n",
                "where $\\epsilon_t \\sim N(0,\\sigma^2)$\n",
                "\n",
                "### Model solution\n",
                "From (2) and (3), we have $$k_{t+1} = e^{z_t}k_t^\\alpha l_t^{1-\\alpha} - c_t + (1-\\delta)k_t$$\n",
                "\n",
                "Lagrangian:\n",
                "$$\n",
                "L = \\max_{c_t, l_t} E_0\\sum_{t=1}^\\infty \\beta^{t-1} [\\frac{(c_t^\\theta (1-l_t)^{1-\\theta})^{1-\\tau}}{1-\\tau} - \\lambda (k_{t+1} - e^{z_t}k_t^\\alpha l_t^{1-\\alpha} - c_t + (1-\\delta)k_t)]\n",
                "$$"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Take derivatives with respect to $k_t$ we have Euler equation\n",
                "$$\n",
                "\\frac{(c_t^\\theta (1-l_t)^{1-\\theta})^{1-\\tau}}{c_t} = \\beta E_t[\\frac{(c_{t+1}^\\theta (1-l_{t+1})^{1-\\theta})^{1-\\tau}}{c_{t+1}} (1+\\alpha e^{z_t}k_t^{\\alpha-1}l_t^{\\alpha} - \\delta)]\n",
                "$$\n",
                "\n",
                "The optimality condition for labor supply is \n",
                "$$\n",
                "\\frac{1-\\theta}{\\theta} \\frac{c_t}{1-l_t} = (1-\\alpha)e^{z_t}k_t^\\alpha l_t^{-\\alpha}\n",
                "$$\n",
                "\n",
                "The equilibrium is characterized by (5), (7) and (8)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Calibration\n",
                "\n",
                "We use the following calibration\n",
                "\n",
                "|parameter|calibration|\n",
                "|---------|-----------|\n",
                "|$\\beta$|0.98|\n",
                "|$\\theta$|0.35|\n",
                "|$\\delta$|0.01|\n",
                "|$\\alpha$|0.4|\n",
                "|$\\tau$|2|\n",
                "|$\\rho$|0.95|\n",
                "|s|0.007|\n",
                "|$\\sigma$|1|\n",
                "\n",
                "### Model approximation"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2019-09-23T00:27:44.612038Z",
                    "start_time": "2019-09-23T00:27:44.597080Z"
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Created file 'C:\\Users\\barry\\Documents\\Academics\\Quantecon-Notes\\Dynare_model\\NGM\\NGM_approx.mod'.\n"
                    ]
                }
            ],
            "source": [
                "%%file Dynare_model\\NGM\\NGM_approx.mod\n",
                "\n",
                "periods = 1000;\n",
                "var c k l z;\n",
                "varexo e;\n",
                "\n",
                "parameters beta theta delta alpha tau rho s;\n",
                "\n",
                "beta = 0.98;\n",
                "theta = 0.35;\n",
                "delta = 0.01;\n",
                "alpha = 0.4;\n",
                "tau = 2;\n",
                "rho = 0.95;\n",
                "s = 0.007;\n",
                "\n",
                "model;\n",
                "(c^theta*(1-l)^(1-theta))^(1-tau)/c = beta*((c(+1)^theta*(1-l(+1))^(1-theta))^(1-tau)/c(+1))*(1+alpha*exp(z(-1))*k(-1)^(alpha-1)*l^(1-alpha)-delta);\n",
                "c = theta/(1-theta)*(1-alpha)*exp(z(-1))*k(-1)^alpha*l^(-alpha)*(1-l);\n",
                "k = exp(z(-1))*k(-1)^alpha*l^(1-alpha) - c + (1-delta)*k(-1);\n",
                "z = rho*z(-1)+s*e;\n",
                "end;\n",
                "\n",
                "initval;\n",
                "k = 1;\n",
                "c = 1;\n",
                "l = 0.3;\n",
                "z = 0;\n",
                "e = 0;\n",
                "end;\n",
                "\n",
                "shocks;\n",
                "var e;\n",
                "stderr 1;\n",
                "end;\n",
                "\n",
                "steady;\n",
                "\n",
                "stoch_simul(dr_algo = 0, periods = 1000); "
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2019-09-23T00:29:09.937296Z",
                    "start_time": "2019-09-23T00:28:58.805881Z"
                },
                "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: NGM_approx.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: NGM_approx.mod:38.13-23: dr_algo option is now deprecated, and may be removed in a future version of Dynare\n",
                        "Found 4 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 1.31218\n",
                        "k \t\t 19.8754\n",
                        "l \t\t 0.271144\n",
                        "z \t\t 0\n",
                        "\n",
                        "MODEL SUMMARY\n",
                        "\n",
                        "  Number of variables:         4\n",
                        "  Number of stochastic shocks: 1\n",
                        "  Number of state variables:   2\n",
                        "  Number of jumpers:           2\n",
                        "  Number of static variables:  0\n",
                        "\n",
                        "\n",
                        "MATRIX OF COVARIANCE OF EXOGENOUS SHOCKS\n",
                        "Variables           e\n",
                        "e            1.000000\n",
                        "\n",
                        "POLICY AND TRANSITION FUNCTIONS\n",
                        "                                   c               k               l               z\n",
                        "Constant                    1.312177       19.875419        0.271145               0\n",
                        "(correction)               -0.000006        0.000012        0.000002               0\n",
                        "k(-1)                       0.037303        0.973355       -0.002916               0\n",
                        "z(-1)                       0.589562        1.568055        0.193416        0.950000\n",
                        "e                           0.002289       -0.004337       -0.000613        0.007000\n",
                        "k(-1),k(-1)                -0.000342       -0.000130        0.000061               0\n",
                        "z(-1),k(-1)                 0.011039        0.028178        0.000826               0\n",
                        "z(-1),z(-1)                 0.230790        1.069247       -0.002947               0\n",
                        "e,e                         0.000005       -0.000009               0               0\n",
                        "k(-1),e                     0.000040       -0.000060        0.000009               0\n",
                        "z(-1),e                     0.001545       -0.003967       -0.000286               0\n",
                        "\n",
                        "\n",
                        "MOMENTS OF SIMULATED VARIABLES\n",
                        "VARIABLE              MEAN       STD. DEV.        VARIANCE        SKEWNESS        KURTOSIS\n",
                        "c                 1.291058        0.034110        0.001163        0.283783        0.313329\n",
                        "k                19.436421        0.678484        0.460341        0.239463        0.182051\n",
                        "l                 0.270979        0.003653        0.000013       -0.254001        0.139136\n",
                        "z                -0.007843        0.022039        0.000486        0.170511        0.259799\n",
                        "\n",
                        "\n",
                        "CORRELATION OF SIMULATED VARIABLES\n",
                        "VARIABLE         c       k       l       z\n",
                        "c           1.0000  0.9576  0.3701  0.7543\n",
                        "k           0.9576  1.0000  0.1132  0.5362\n",
                        "l           0.3701  0.1132  1.0000  0.7771\n",
                        "z           0.7543  0.5362  0.7771  1.0000\n",
                        "\n",
                        "\n",
                        "AUTOCORRELATION OF SIMULATED VARIABLES\n",
                        "VARIABLE         1       2       3       4       5\n",
                        "c           0.9954  0.9878  0.9789  0.9693  0.9592\n",
                        "k           0.9980  0.9955  0.9916  0.9863  0.9797\n",
                        "l           0.8455  0.7789  0.7046  0.6464  0.5951\n",
                        "z           0.9496  0.8989  0.8467  0.8006  0.7597\n",
                        "Total computing time : 0h00m08s\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"
                }
            ],
            "source": [
                "cd 'NGM'\n",
                "dynare NGM_approx"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## International Business Cycle Model\n",
                "Kim and Kim (2003)\n",
                "\n",
                "### Model\n",
                "Two countries are identical ex-ante and markets are complete. A solution to a Pareto planner's problem is characterized by the following equations\n",
                "\n",
                "$$\n",
                "C_{1,t} = C_{2,t}\n",
                "$$\n",
                "\n",
                "$$\n",
                "C_{1,t}^{-\\gamma} = \\beta E_t C_{1,t+1}^{-\\gamma} (\\alpha A_{1,t+1} K_{1,t+1}^{\\alpha-1} +1-\\delta)\n",
                "$$\n",
                "\n",
                "$$\n",
                "C_{2,t}^{-\\gamma} = \\beta E_t C_{2,t+1}^{-\\gamma} (\\alpha A_{2,t+1} K_{2,t+1}^{\\alpha-1} +1-\\delta)\n",
                "$$\n",
                "$$\n",
                "A_{1,t}K_{1,t}^\\alpha + A_{2,t}K_{2,t}^\\alpha = C_{1,t}+ C_{2,t} + K_{1,t+1} - (1-\\delta)K_{1,t} + K_{2,t+1} - (1-\\delta)K_{2,t}\n",
                "$$\n",
                "$$\n",
                "\\ln{A_{1,t+1}} = \\rho \\ln{A_{1,t}} + \\epsilon_{1,t+1}\n",
                "$$\n",
                "\n",
                "$$\n",
                "\\ln{A_{2,t+1}} = \\rho \\ln{A_{2,t}} + \\epsilon_{2,t+1}\n",
                "$$\n",
                "\n",
                "The first three equations are the first-order conditions with regards to the consumption and the fourth one is the world resource constraint. The last two describe the law of motion for the technological shock.\n",
                "\n",
                "### Calibration\n",
                "We use the following calibration\n",
                "\n",
                "|Parameter|Calibration|\n",
                "|---------|-----------|\n",
                "|$\\beta$| 0.98|\n",
                "|$\\delta$|0.005|\n",
                "|$\\alpha$|0.4|\n",
                "|$\\rho$|0.85|\n",
                "|$\\sigma_1$|0.08|\n",
                "|$\\sigma_2$|0.08|\n",
                "\n",
                "Dynare computes the policy function as a second-order approximation around the (log) steady-state"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 53,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2019-08-18T19:08:32.222715Z",
                    "start_time": "2019-08-18T19:08:32.190804Z"
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "\n"
                    ]
                }
            ],
            "source": [
                "cd 'C:\\Users\\barry\\Documents\\Academics\\Quantecon-Notes'"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 54,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2019-08-18T19:08:37.616456Z",
                    "start_time": "2019-08-18T19:08:37.606482Z"
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Created file 'C:\\Users\\barry\\Documents\\Academics\\Quantecon-Notes\\Dynare_model\\International_BC\\International_BC_approx.mod'.\n"
                    ]
                }
            ],
            "source": [
                "%%file International_BC\\International_BC_approx.mod\n",
                "periods 1000;\n",
                "var c1 c2 k1 k2 a1 a2;\n",
                "varexo e1 e2;\n",
                "\n",
                "parameters gamma delta alpha beta rho;\n",
                "\n",
                "gamma = 2;\n",
                "delta = 0.05;\n",
                "alpha = 0.4;\n",
                "beta = 0.98;\n",
                "rho = 0.85;\n",
                "\n",
                "model;\n",
                "c1 = c2;\n",
                "exp(c1)^(-gamma) = beta*exp(c1(+1))^(-gamma)*(alpha*exp(a1(+1))*exp(k1)^(alpha-1)+1-delta);\n",
                "exp(c2)^(-gamma) = beta*exp(c2(+1))^(-gamma)*(alpha*exp(a2(+1))*exp(k2)^(alpha-1)+1-delta);\n",
                "exp(c1) + exp(c2)+exp(k1) - exp(k1(-1))*(1-delta) + exp(k2) - exp(k2(-1))*(1-delta)=exp(a1)*exp(k1(-1))^alpha + exp(a2)*exp(k2(-1))^alpha;\n",
                "a1 = rho*a1(-1)+e1;\n",
                "a2 = rho*a2(-1)+e2;\n",
                "end;\n",
                "\n",
                "initval;\n",
                "k1 = 2.8;\n",
                "k2 = 2.8;\n",
                "c1 = 0.8;\n",
                "c2 = 0.8;\n",
                "a1 = 0;\n",
                "a2 = 0;\n",
                "e1 = 0;\n",
                "e2 = 0;\n",
                "end;\n",
                "\n",
                "shocks;\n",
                "var e1;\n",
                "stderr 0.08;\n",
                "var e2;\n",
                "stderr 0.08;\n",
                "end;\n",
                "\n",
                "steady;\n",
                "\n",
                "stoch_simul(periods = 1000);"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 55,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2019-08-18T19:08:47.763415Z",
                    "start_time": "2019-08-18T19:08:47.735489Z"
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "\n"
                    ]
                }
            ],
            "source": [
                "cd 'Dynare_model'\n",
                "cd 'International_BC'"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 57,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2019-08-18T19:09:07.656185Z",
                    "start_time": "2019-08-18T19:09:02.262047Z"
                }
            },
            "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: International_BC_approx.mod:1.1-13: 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",
                        "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",
                        "c1 \t\t 0.823947\n",
                        "c2 \t\t 0.823947\n",
                        "k1 \t\t 2.89526\n",
                        "k2 \t\t 2.89526\n",
                        "a1 \t\t 0\n",
                        "a2 \t\t 0\n",
                        "\n",
                        "MODEL SUMMARY\n",
                        "\n",
                        "  Number of variables:         6\n",
                        "  Number of stochastic shocks: 2\n",
                        "  Number of state variables:   4\n",
                        "  Number of jumpers:           4\n",
                        "  Number of static variables:  0\n",
                        "\n",
                        "\n",
                        "MATRIX OF COVARIANCE OF EXOGENOUS SHOCKS\n",
                        "Variables          e1        e2\n",
                        "e1           0.006400  0.000000\n",
                        "e2           0.000000  0.006400\n",
                        "\n",
                        "POLICY AND TRANSITION FUNCTIONS\n",
                        "                                  c1              c2              k1              k2              a1              a2\n",
                        "Constant                    0.828143        0.828143        2.894730        2.894730               0               0\n",
                        "(correction)                0.004196        0.004196       -0.000529       -0.000529               0               0\n",
                        "k1(-1)                      0.243036        0.243036        0.479577        0.479577               0               0\n",
                        "k2(-1)                      0.243036        0.243036        0.479577        0.479577               0               0\n",
                        "a1(-1)                      0.118991        0.118991        0.661897       -0.542270        0.850000               0\n",
                        "a2(-1)                      0.118991        0.118991       -0.542270        0.661897               0        0.850000\n",
                        "e1                          0.139989        0.139989        0.778702       -0.637965        1.000000               0\n",
                        "e2                          0.139989        0.139989       -0.637965        0.778702               0        1.000000\n",
                        "k1(-1),k1(-1)               0.061511        0.061511        0.118070        0.118070               0               0\n",
                        "k2(-1),k1(-1)              -0.109951       -0.109951       -0.223581       -0.223581               0               0\n",
                        "k2(-1),k2(-1)               0.061511        0.061511        0.118070        0.118070               0               0\n",
                        "a1(-1),k1(-1)              -0.013166       -0.013166       -0.000747       -0.000747               0               0\n",
                        "a1(-1),k2(-1)              -0.027420       -0.027420       -0.028874       -0.028874               0               0\n",
                        "a1(-1),a1(-1)               0.032025        0.032025       -0.156175       -0.156175               0               0\n",
                        "a2(-1),k1(-1)              -0.027420       -0.027420       -0.028874       -0.028874               0               0\n",
                        "a2(-1),k2(-1)              -0.013166       -0.013166       -0.000747       -0.000747               0               0\n",
                        "a2(-1),a1(-1)              -0.029124       -0.029124        0.360813        0.360813               0               0\n",
                        "a2(-1),a2(-1)               0.032025        0.032025       -0.156175       -0.156175               0               0\n",
                        "e1,e1                       0.044325        0.044325       -0.216160       -0.216160               0               0\n",
                        "e2,e1                      -0.040310       -0.040310        0.499395        0.499395               0               0\n",
                        "e2,e2                       0.044325        0.044325       -0.216160       -0.216160               0               0\n",
                        "k1(-1),e1                  -0.015489       -0.015489       -0.000879       -0.000879               0               0\n",
                        "k1(-1),e2                  -0.032259       -0.032259       -0.033969       -0.033969               0               0\n",
                        "k2(-1),e1                  -0.032259       -0.032259       -0.033969       -0.033969               0               0\n",
                        "k2(-1),e2                  -0.015489       -0.015489       -0.000879       -0.000879               0               0\n",
                        "a1(-1),e1                   0.075353        0.075353       -0.367471       -0.367471               0               0\n",
                        "a1(-1),e2                  -0.034263       -0.034263        0.424485        0.424485               0               0\n",
                        "a2(-1),e1                  -0.034263       -0.034263        0.424485        0.424485               0               0\n",
                        "a2(-1),e2                   0.075353        0.075353       -0.367471       -0.367471               0               0\n",
                        "\n",
                        "\n",
                        "MOMENTS OF SIMULATED VARIABLES\n",
                        "VARIABLE              MEAN       STD. DEV.        VARIANCE        SKEWNESS        KURTOSIS\n",
                        "c1                0.839663        0.112373        0.012628        0.383347        0.667008\n",
                        "c2                0.839663        0.112373        0.012628        0.383347        0.667008\n",
                        "k1                2.937649        0.235513        0.055467        0.036596       -0.164785\n",
                        "k2                2.875717        0.238655        0.056956        0.147170        0.289925\n",
                        "a1                0.021386        0.149715        0.022415        0.041882       -0.072424\n",
                        "a2               -0.022331        0.151984        0.023099       -0.164757       -0.020458\n",
                        "\n",
                        "\n",
                        "CORRELATION OF SIMULATED VARIABLES\n",
                        "VARIABLE        c1      c2      k1      k2      a1      a2\n",
                        "c1          1.0000  1.0000  0.8168  0.7967  0.5239  0.5057\n",
                        "c2          1.0000  1.0000  0.8168  0.7967  0.5239  0.5057\n",
                        "k1          0.8168  0.8168  1.0000  0.3535  0.6837 -0.0285\n",
                        "k2          0.7967  0.7967  0.3535  1.0000 -0.0297  0.6925\n",
                        "a1          0.5239  0.5239  0.6837 -0.0297  1.0000  0.2043\n",
                        "a2          0.5057  0.5057 -0.0285  0.6925  0.2043  1.0000\n",
                        "\n",
                        "\n",
                        "AUTOCORRELATION OF SIMULATED VARIABLES\n",
                        "VARIABLE         1       2       3       4       5\n",
                        "c1          0.9881  0.9737  0.9581  0.9403  0.9204\n",
                        "c2          0.9881  0.9737  0.9581  0.9403  0.9204\n",
                        "k1          0.9421  0.8884  0.8464  0.8018  0.7636\n",
                        "k2          0.9373  0.8794  0.8333  0.7861  0.7451\n",
                        "a1          0.8436  0.6978  0.5853  0.4866  0.4010\n",
                        "a2          0.8580  0.7361  0.6519  0.5670  0.4883\n",
                        "\n",
                        "\n",
                        "\n",
                        "VARIANCE DECOMPOSITION SIMULATING ONE SHOCK AT A TIME (in percent)\n",
                        "                    e1                e2  Tot. lin. contr.\n",
                        "c1               38.20             34.55             72.74\n",
                        "c2               38.20             34.55             72.74\n",
                        "k1               66.67             22.49             89.16\n",
                        "k2               24.10             64.11             88.21\n",
                        "a1              100.11              0.00            100.11\n",
                        "a2                0.00            100.11            100.11\n",
                        "Note: numbers do not add up to 100 due to i) non-zero correlation of simulated shocks in small samples and ii) nonlinearity\n",
                        "\n",
                        "Total computing time : 0h00m04s\n",
                        "Note: 1 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 International_BC_approx.mod"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Fiscal policies in the growth model\n",
                "\n",
                "Ljungqvist and Sargent (2004)\n",
                "\n",
                "### The model\n",
                "\n",
                "A deterministic growth model with exogenous stream of government expenditure, and several kinds of distorting taxeds. \n",
                "\n",
                "A representative agent maximizes\n",
                "$$\n",
                "\\sum_{t=0}^\\infty \\beta^t \\frac{c_t^{1-\\gamma}}{1-\\gamma}\n",
                "$$\n",
                "\n",
                "Feasible allocation:\n",
                "$$\n",
                "g_t +c_t+k_t \\leq Ak_t^\\alpha + (1-\\delta)k_t\n",
                "$$\n",
                "Household budget constraint\n",
                "$$\n",
                "\\sum_{t=0}^\\infty [q_t(1+\\tau_{ct})+(1-\\tau_{it})q_t [k_{t+1} - (1-\\delta)k_t]]\\leq \\sum_{t=0}^\\infty [r_t(1-\\tau_{kt})k_t+w_t]\n",
                "$$\n",
                "and the government budget constraint\n",
                "$$\n",
                "\\sum_{t=0}^\\infty q_tg_t\\leq \\sum_{t=0}^\\infty [\\tau_{ct}q_tc_t-\\tau_{it}q_t[k_{t+1}-(1-\\delta)k_t]+r_t\\tau_{kt}kt+w_t]\n",
                "$$\n",
                "\n",
                "The equilibrium is characterized as\n",
                "$$c_t = Ak_t^\\alpha + (1-\\delta)k_t-k_{t+1}-g_t\n",
                "$$\n",
                "$$q_t = \\beta^tc_t^{-\\gamma}/(1+\\tau_{ct})\n",
                "$$\n",
                "$$r_t/q_t = A\\alpha k_t^{\\alpha-1}\n",
                "$$\n",
                "$$w_t/q_t = Ak_t^\\alpha - k_t A\\alpha k_t^{\\alpha-1}\n",
                "$$\n",
                "$$R_t = \\frac{1+\\tau_{ct}}{1+\\tau_{ct+1}}[\\frac{1+\\tau_{it+1}}{1+\\tau_{it}}(1-\\delta) + \\frac{1+\\tau_{kt+1}}{1+\\tau_{kt}}A\\alpha k_{t+1}^{\\alpha-1}]\n",
                "$$\n",
                "$$\n",
                "s_t/q_t = (1-\\tau_{kt})A\\alpha k_t^{\\alpha-1} + (1-\\delta)\n",
                "$$\n",
                "$$\n",
                "c_t^{-\\gamma} = \\beta c_{t+1}^{-\\gamma}R_{t+1}\n",
                "$$\n",
                "\n",
                "### Calibration\n",
                "We use the following calibration\n",
                "\n",
                "|Parameter|Calibration|\n",
                "|---------|-----------|\n",
                "|$\\beta$| 0.98|\n",
                "|$\\delta$|0.2|\n",
                "|$A$|1|\n",
                "|$\\alpha$|0.33|\n",
                "|$\\gamma$|2|\n",
                "|$g$|0.2|\n",
                "|$\\tau_c$|0|\n",
                "|$\\tau_i$|0|\n",
                "|$\\tau_k$|0|\n",
                "\n",
                "### Model solution"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 15,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2019-09-25T01:59:24.581417Z",
                    "start_time": "2019-09-25T01:59:24.556517Z"
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "\n"
                    ]
                }
            ],
            "source": [
                "cd 'C:\\Users\\barry\\Documents\\Academics\\Quantecon-Notes'"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "A permanent increase in g"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 23,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2019-09-28T00:40:56.305765Z",
                    "start_time": "2019-09-28T00:40:56.286843Z"
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Created file 'C:\\Users\\barry\\Documents\\Academics\\Quantecon-Notes\\Dynare_model\\fiscal_policy_growth\\permanent_increase_g.mod'.\n"
                    ]
                }
            ],
            "source": [
                "%%file Dynare_model\\fiscal_policy_growth\\permanent_increase_g.mod\n",
                "\n",
                "\n",
                "var c k;\n",
                "varexo taui tauc tauk g;\n",
                "parameters bet gam del alpha A;\n",
                "bet=.95; \n",
                "gam=2; \n",
                "del=.2; \n",
                "alpha=.33; \n",
                "A=1; \n",
                "\n",
                "\n",
                "model;\n",
                "k=A*k(-1)^alpha+(1-del)*k(-1)-c-g;\n",
                "c^(-gam)=bet*(c(+1)^(-gam))*((1+tauc(-1))/(1+tauc))*((1-taui)*(1-del)/(1-taui(-1))+((1-tauk)/(1-taui(-1)))*alpha*A*k(-1)^(alpha-1));\n",
                "end;\n",
                "\n",
                "initval; \n",
                "k=1.5; \n",
                "c=0.6; \n",
                "g = 0.2; \n",
                "tauc = 0; \n",
                "taui = 0; \n",
                "tauk = 0; \n",
                "end;\n",
                "steady; \n",
                "\n",
                "endval;\n",
                "k=1.5;\n",
                "c=0.4; \n",
                "g =0.4; \n",
                "tauc =0; \n",
                "taui =0; \n",
                "tauk =0; end;\n",
                "steady;\n",
                "\n",
                "shocks;\n",
                "var g; \n",
                "periods 1:9; \n",
                "values 0.2; \n",
                "end;\n",
                "\n",
                "simul(periods=100);\n",
                "\n",
                "co=ys0_(1);\n",
                "ko = ys0_(2);\n",
                "go = ex0_(1,1);\n",
                "\n",
                "rbig0=1/bet;\n",
                "rbig=c(2:101).^(-gam)./(bet*c(3:102).^(-gam));\n",
                "rq0=alpha*A*ko^(alpha-1);\n",
                "rq=alpha*A*k(1:100).^(alpha-1);\n",
                "wq0=A*ko^alpha-ko*alpha*A*ko^(alpha-1);\n",
                "wq=A*k(1:100).^alpha-k(1:100).*alpha*A.*k(1:100).^(alpha-1);\n",
                "//sq0=(1-ex_(1,4))*A*alpha*ko^(alpha-1)+(1-del);\n",
                "//sq=(1-ex_(1:100,4)')*A*alpha.*k(1:100).^(alpha-1)+(1-del);\n",
                "\n",
                "figure\n",
                "subplot(2,3,1)\n",
                "plot([ko*ones(100,1)  k(1:100) ])\n",
                "title('k')\n",
                "subplot(2,3,2)\n",
                "plot([co*ones(100,1)  c(2:101) ])\n",
                "title('c')\n",
                "subplot(2,3,3)\n",
                "plot([rbig0*ones(100,1) rbig ])\n",
                "title('R')\n",
                "subplot(2,3,4)\n",
                "plot([wq0*ones(100,1) wq ])\n",
                "title('w/q')\n",
                "//subplot(2,3,5)\n",
                "//plot([sq0*ones(100,1) sq ])\n",
                "//title('s/q')\n",
                "subplot(2,3,5)\n",
                "plot([rq0*ones(100,1) rq ])\n",
                "title('r/q')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 24,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2019-09-28T00:40:57.367382Z",
                    "start_time": "2019-09-28T00:40:57.256653Z"
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "\n"
                    ]
                }
            ],
            "source": [
                "cd 'C:\\Users\\barry\\Documents\\Academics\\Quantecon-Notes'\n",
                "cd 'Dynare_model'\n",
                "cd 'fiscal_policy_growth'"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 25,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2019-09-28T00:41:01.391825Z",
                    "start_time": "2019-09-28T00:40:59.029895Z"
                }
            },
            "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",
                        "Found 2 equation(s).\n",
                        "Evaluating expressions...done\n",
                        "Computing static model derivatives:\n",
                        " - order 1\n",
                        "Computing dynamic model derivatives:\n",
                        " - order 1\n",
                        "Processing outputs ...\n",
                        "done\n",
                        "Preprocessing completed.\n",
                        "\n",
                        "\n",
                        "STEADY-STATE RESULTS:\n",
                        "\n",
                        "c \t\t 0.642645\n",
                        "k \t\t 1.48996\n",
                        "\n",
                        "STEADY-STATE RESULTS:\n",
                        "\n",
                        "c \t\t 0.442645\n",
                        "k \t\t 1.48995\n",
                        "\n",
                        "--------------------------------------------------------\n",
                        "MODEL SIMULATION:\n",
                        "\n",
                        "Iter: 1,\t err. = 0.2, \t time = 0.017\n",
                        "Iter: 2,\t err. = 0.016713, \t time = 0.006\n",
                        "Iter: 3,\t err. = 0.00038444, \t time = 0.001\n",
                        "Iter: 4,\t err. = 1.3226e-07, \t time = 0.002\n",
                        "\n",
                        "Total time of simulation: 0.037\n",
                        "--------------------------------------------------------\n",
                        "\n",
                        "Perfect foresight solution found.\n",
                        "\n",
                        "Total computing time : 0h00m00s\n",
                        "\n"
                    ]
                },
                {
                    "data": {
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\n",
                        "text/plain": [
                            "<IPython.core.display.Image object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "dynare permanent_increase_g.mod"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "A permanent increase in $\\tau_c$"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 28,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2019-09-28T00:44:33.100485Z",
                    "start_time": "2019-09-28T00:44:33.087375Z"
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Created file 'C:\\Users\\barry\\Documents\\Academics\\Quantecon-Notes\\Dynare_model\\fiscal_policy_growth\\permanent_increase_tau_c.mod'.\n"
                    ]
                }
            ],
            "source": [
                "%%file Dynare_model\\fiscal_policy_growth\\permanent_increase_tau_c.mod\n",
                "\n",
                "var c k;\n",
                "varexo taui tauc tauk g;\n",
                "parameters bet gam del alpha A;\n",
                "bet=.95; \n",
                "gam=2; \n",
                "del=.2; \n",
                "alpha=.33; \n",
                "A=1; \n",
                "\n",
                "\n",
                "model;\n",
                "k=A*k(-1)^alpha+(1-del)*k(-1)-c-g;\n",
                "c^(-gam)=bet*(c(+1)^(-gam))*((1+tauc(-1))/(1+tauc))*((1-taui)*(1-del)/(1-taui(-1))+((1-tauk)/(1-taui(-1)))*alpha*A*k(-1)^(alpha-1));\n",
                "end;\n",
                "\n",
                "initval; \n",
                "k=1.5; \n",
                "c=0.6; \n",
                "g = 0.2; \n",
                "tauc = 0; \n",
                "taui = 0; \n",
                "tauk = 0; \n",
                "end;\n",
                "steady; \n",
                "\n",
                "endval;\n",
                "k=1.5;\n",
                "c=0.4; \n",
                "g =0.2; \n",
                "tauc =0.2; \n",
                "taui =0; \n",
                "tauk =0; end;\n",
                "steady;\n",
                "\n",
                "shocks;\n",
                "var tauc; \n",
                "periods 1:9; \n",
                "values 0; \n",
                "end;\n",
                "\n",
                "simul(periods=100);\n",
                "\n",
                "co=ys0_(1);\n",
                "ko = ys0_(2);\n",
                "go = ex0_(1,1);\n",
                "\n",
                "rbig0=1/bet;\n",
                "rbig=c(2:101).^(-gam)./(bet*c(3:102).^(-gam));\n",
                "rq0=alpha*A*ko^(alpha-1);\n",
                "rq=alpha*A*k(1:100).^(alpha-1);\n",
                "wq0=A*ko^alpha-ko*alpha*A*ko^(alpha-1);\n",
                "wq=A*k(1:100).^alpha-k(1:100).*alpha*A.*k(1:100).^(alpha-1);\n",
                "//sq0=(1-ex_(1,4))*A*alpha*ko^(alpha-1)+(1-del);\n",
                "//sq=(1-ex_(1:100,4)')*A*alpha.*k(1:100).^(alpha-1)+(1-del);\n",
                "\n",
                "figure\n",
                "subplot(2,3,1)\n",
                "plot([ko*ones(100,1)  k(1:100) ])\n",
                "title('k')\n",
                "subplot(2,3,2)\n",
                "plot([co*ones(100,1)  c(2:101) ])\n",
                "title('c')\n",
                "subplot(2,3,3)\n",
                "plot([rbig0*ones(100,1) rbig ])\n",
                "title('R')\n",
                "subplot(2,3,4)\n",
                "plot([wq0*ones(100,1) wq ])\n",
                "title('w/q')\n",
                "//subplot(2,3,5)\n",
                "//plot([sq0*ones(100,1) sq ])\n",
                "//title('s/q')\n",
                "subplot(2,3,5)\n",
                "plot([rq0*ones(100,1) rq ])\n",
                "title('r/q')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 29,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2019-09-28T00:44:36.084158Z",
                    "start_time": "2019-09-28T00:44:34.245962Z"
                }
            },
            "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",
                        "Found 2 equation(s).\n",
                        "Evaluating expressions...done\n",
                        "Computing static model derivatives:\n",
                        " - order 1\n",
                        "Computing dynamic model derivatives:\n",
                        " - order 1\n",
                        "Processing outputs ...\n",
                        "done\n",
                        "Preprocessing completed.\n",
                        "\n",
                        "\n",
                        "STEADY-STATE RESULTS:\n",
                        "\n",
                        "c \t\t 0.642645\n",
                        "k \t\t 1.48996\n",
                        "\n",
                        "STEADY-STATE RESULTS:\n",
                        "\n",
                        "c \t\t 0.642645\n",
                        "k \t\t 1.48996\n",
                        "\n",
                        "--------------------------------------------------------\n",
                        "MODEL SIMULATION:\n",
                        "\n",
                        "Iter: 1,\t err. = 0.40356, \t time = 0.01\n",
                        "Iter: 2,\t err. = 0.014566, \t time = 0.002\n",
                        "Iter: 3,\t err. = 1.5284e-05, \t time = 0.003\n",
                        "Iter: 4,\t err. = 9.3122e-11, \t time = 0.002\n",
                        "\n",
                        "Total time of simulation: 0.019\n",
                        "--------------------------------------------------------\n",
                        "\n",
                        "Perfect foresight solution found.\n",
                        "\n",
                        "Total computing time : 0h00m00s\n",
                        "\n"
                    ]
                },
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": [
                            "<IPython.core.display.Image object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "dynare permanent_increase_tau_c.mod"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "One time increase in g"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 30,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2019-09-28T00:46:07.964695Z",
                    "start_time": "2019-09-28T00:46:07.951760Z"
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Created file 'C:\\Users\\barry\\Documents\\Academics\\Quantecon-Notes\\Dynare_model\\fiscal_policy_growth\\transit_increase_g.mod'.\n"
                    ]
                }
            ],
            "source": [
                "%%file Dynare_model\\fiscal_policy_growth\\transit_increase_g.mod\n",
                "\n",
                "\n",
                "var c k;\n",
                "varexo taui tauc tauk g;\n",
                "parameters bet gam del alpha A;\n",
                "bet=.95; \n",
                "gam=2; \n",
                "del=.2; \n",
                "alpha=.33; \n",
                "A=1; \n",
                "\n",
                "\n",
                "model;\n",
                "k=A*k(-1)^alpha+(1-del)*k(-1)-c-g;\n",
                "c^(-gam)=bet*(c(+1)^(-gam))*((1+tauc(-1))/(1+tauc))*((1-taui)*(1-del)/(1-taui(-1))+((1-tauk)/(1-taui(-1)))*alpha*A*k(-1)^(alpha-1));\n",
                "end;\n",
                "\n",
                "initval; \n",
                "k=1.5; \n",
                "c=0.6; \n",
                "g = 0.2; \n",
                "tauc = 0; \n",
                "taui = 0; \n",
                "tauk = 0; \n",
                "end;\n",
                "steady; \n",
                "\n",
                "endval;\n",
                "k=1.5;\n",
                "c=0.6; \n",
                "g =0.4; \n",
                "tauc =0; \n",
                "taui =0; \n",
                "tauk =0; end;\n",
                "steady;\n",
                "\n",
                "shocks;\n",
                "var g; \n",
                "periods 10; \n",
                "values 0.2; \n",
                "end;\n",
                "\n",
                "simul(periods=100);\n",
                "\n",
                "co=ys0_(1);\n",
                "ko = ys0_(2);\n",
                "go = ex0_(1,1);\n",
                "\n",
                "rbig0=1/bet;\n",
                "rbig=c(2:101).^(-gam)./(bet*c(3:102).^(-gam));\n",
                "rq0=alpha*A*ko^(alpha-1);\n",
                "rq=alpha*A*k(1:100).^(alpha-1);\n",
                "wq0=A*ko^alpha-ko*alpha*A*ko^(alpha-1);\n",
                "wq=A*k(1:100).^alpha-k(1:100).*alpha*A.*k(1:100).^(alpha-1);\n",
                "//sq0=(1-ex_(1,4))*A*alpha*ko^(alpha-1)+(1-del);\n",
                "//sq=(1-ex_(1:100,4)')*A*alpha.*k(1:100).^(alpha-1)+(1-del);\n",
                "\n",
                "figure\n",
                "subplot(2,3,1)\n",
                "plot([ko*ones(100,1)  k(1:100) ])\n",
                "title('k')\n",
                "subplot(2,3,2)\n",
                "plot([co*ones(100,1)  c(2:101) ])\n",
                "title('c')\n",
                "subplot(2,3,3)\n",
                "plot([rbig0*ones(100,1) rbig ])\n",
                "title('R')\n",
                "subplot(2,3,4)\n",
                "plot([wq0*ones(100,1) wq ])\n",
                "title('w/q')\n",
                "//subplot(2,3,5)\n",
                "//plot([sq0*ones(100,1) sq ])\n",
                "//title('s/q')\n",
                "subplot(2,3,5)\n",
                "plot([rq0*ones(100,1) rq ])\n",
                "title('r/q')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 31,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2019-09-28T00:46:25.839908Z",
                    "start_time": "2019-09-28T00:46:23.902800Z"
                }
            },
            "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",
                        "Found 2 equation(s).\n",
                        "Evaluating expressions...done\n",
                        "Computing static model derivatives:\n",
                        " - order 1\n",
                        "Computing dynamic model derivatives:\n",
                        " - order 1\n",
                        "Processing outputs ...\n",
                        "done\n",
                        "Preprocessing completed.\n",
                        "\n",
                        "\n",
                        "STEADY-STATE RESULTS:\n",
                        "\n",
                        "c \t\t 0.642645\n",
                        "k \t\t 1.48996\n",
                        "\n",
                        "STEADY-STATE RESULTS:\n",
                        "\n",
                        "c \t\t 0.442645\n",
                        "k \t\t 1.48996\n",
                        "\n",
                        "--------------------------------------------------------\n",
                        "MODEL SIMULATION:\n",
                        "\n",
                        "Iter: 1,\t err. = 0.2, \t time = 0.008\n",
                        "Iter: 2,\t err. = 0.0039476, \t time = 0.002\n",
                        "Iter: 3,\t err. = 1.1244e-06, \t time = 0.002\n",
                        "\n",
                        "Total time of simulation: 0.015\n",
                        "--------------------------------------------------------\n",
                        "\n",
                        "Perfect foresight solution found.\n",
                        "\n",
                        "Total computing time : 0h00m00s\n",
                        "\n"
                    ]
                },
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": [
                            "<IPython.core.display.Image object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "dynare transit_increase_g.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
}