{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f276fef8-0980-40f6-b139-d9b56e896975",
   "metadata": {},
   "source": [
    "# 1.6 RBC Model in Python"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db9baa6a-9410-4c61-a034-973b89fa3831",
   "metadata": {},
   "source": [
    "The algorithm is the same as that we ues in MATLAB case."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a04d4e81-e3b1-4c31-a408-a96daa2ef2d3",
   "metadata": {},
   "source": [
    "## Import Module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cab0f600-794a-48bb-acba-84a9e76a9a56",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install autograd  # if you use autograd for the first time\n",
    "import autograd.numpy as np\n",
    "import math\n",
    "from autograd import jacobian\n",
    "np.set_printoptions(suppress=True,precision=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9de0657-dfbd-448c-bbb9-fdd0b84aeace",
   "metadata": {},
   "source": [
    "__import autograd.numpy as np__: This imports the numpy library but uses Autograd's version, which allows for automatic differentiation. This is useful for optimization and sensitivity analysis.\n",
    "\n",
    "__np.set_printoptions(suppress=True, precision=4)__: This sets the printing options for NumPy arrays, suppressing scientific notation and limiting the precision of printed numbers to 4 decimal places."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04fff3cc-ec9a-40f8-8788-7acb57fec0d0",
   "metadata": {},
   "source": [
    "## Indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0751df4-2cf0-4347-b0d0-8712d4fd1c96",
   "metadata": {},
   "outputs": [],
   "source": [
    "nX = 8\n",
    "nEps = 1\n",
    "iA, iR, iN, iK, iY, iC, iI, iW = range(nX)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1c58018-e1c6-4656-acd5-184d183b2154",
   "metadata": {},
   "source": [
    "__nX = 8__: This defines the number of state variables in the model (5 in this case).\n",
    "\n",
    "__nEps = 1__: This defines the number of shocks (1 in this case).\n",
    "\n",
    "__iA, iR, iN, iK, iY, iC, iI, iW = range(nX)__: This creates indices for the state variables:\n",
    "\n",
    "iA (technology level), iR (interest rate), iN (working hour), iK (capital), iY (output), iC (consumption), iI (investment), iW (wage)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e387854d-9207-48e2-968e-57d96224ff9c",
   "metadata": {},
   "source": [
    "## Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9ca4397-8447-4fb4-b9d4-faa1e2e06f89",
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha = 0.35    # capital share\n",
    "beta = 0.99     # discount factor\n",
    "gamma = 1       # risk aversion\n",
    "eta = 1         # labor elasticity\n",
    "delta = 0.025   # depreciation rate\n",
    "rho = 0.95      # TFP persistency\n",
    "phi = 7.8       # labor disutility"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83b47848-fc0b-4d49-8f1b-b2f76a8e914a",
   "metadata": {},
   "source": [
    "Model parameters are defined:\n",
    "\n",
    "    alpha: Output elasticity of capital.\n",
    "    beta: Discount factor for future utility.\n",
    "    gamma: Coefficient of relative risk aversion.\n",
    "    delta: Depreciation rate of capital.\n",
    "    rho: Persistence of technology shocks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c2a76ba-0cc7-4ea0-a6fa-109afe6c4b13",
   "metadata": {},
   "outputs": [],
   "source": [
    "def SteadyState():\n",
    "    A = 1\n",
    "    R = 1 / beta - 1 + delta\n",
    "    N = 1/3\n",
    "    K = N * (R/A/alpha)**(1/(alpha-1))\n",
    "    Y = A * K**alpha * N**(1-alpha)\n",
    "    C = Y - delta * K\n",
    "    I = delta*K\n",
    "    W = (1-alpha)*Y/N\n",
    "\n",
    "    X = np.zeros(nX)\n",
    "    X[[iA, iR, iN, iK, iY, iC, iI, iW]] = (A, R, N, K, Y, C, I, W)\n",
    "    return X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b652fca-d8a4-4c38-b094-de84b93a28c9",
   "metadata": {},
   "source": [
    "__def SteadyState()__: This defines a function to calculate the steady state of the model.\n",
    "\n",
    "__A = 1.__: Sets the technology level (Z) to 1.\n",
    "\n",
    "__R = 1 / beta - 1 + delta__: Calculates the steady-state interest rate.\n",
    "\n",
    "__K = N * (R/A/alpha)^(1/(alpha-1))__: Calculates the steady-state capital stock using the given parameters.\n",
    "\n",
    "__Y = A * K^alpha * N^(1-alpha)__: Calculates output (Y) using the Cobb-Douglas production function.\n",
    "\n",
    "__C = Y - delta * K__: Calculates consumption (C) as the output minus depreciation on capital.\n",
    "\n",
    "__X = np.zeros(nX)__: Initializes a vector X of zeros to store state variables.\n",
    "\n",
    "__X[[iA, iR, iN, iK, iY, iC, iI, iW]] = (A, R, N, K, Y, C, I, W)__: Assigns the calculated values to the corresponding indices in X."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af85af62-91f7-410c-878a-f684dd69154e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Steady state: [ 1.      0.0351  0.3333 11.4661  1.1499  0.8633  0.2867  2.2423]\n"
     ]
    }
   ],
   "source": [
    "X_SS = SteadyState()\n",
    "epsilon_SS = 0.0\n",
    "print(\"Steady state: {}\".format(X_SS))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51130bc9-2993-4e4e-8ce7-8d82236c967f",
   "metadata": {},
   "source": [
    "__X_SS = SteadyState()__: Calls the SteadyState function and stores the result in X_SS.\n",
    "\n",
    "__epsilon_SS = 0.0__: Initializes the steady-state value of the shock to zero."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fb280d6-93d3-40a2-a3ae-87e6d5992438",
   "metadata": {},
   "source": [
    "## Model equations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d0eb430-dcea-4820-b40f-1e84712e6132",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def F(X_Lag, X, X_Prime, epsilon):\n",
    "\n",
    "    # Unpack. The state variables are unpacked from the input vectors X_Lag, X, and X_Prime.\n",
    "    A, R, N, K, Y, C, I, W = X\n",
    "    A_L, R_L, N_L, K_L, Y_L, C_L, I_L, W_L = X_Lag\n",
    "    A_P, R_P, N_P, K_P, Y_P, C_P, I_P, W_P = X_Prime\n",
    "\n",
    "    return np.hstack((\n",
    "        beta * (1+R-delta) * C_P**(-1/gamma) * C**(1/gamma) - 1.0, # Euler equation\n",
    "        phi * N**(1/eta) - W*C**(-1/gamma),   \n",
    "        A * K_L**alpha * N**(1-alpha) - Y, # Production function\n",
    "        alpha * A * K_L**(alpha-1) * N**(1-alpha) - R, # MPK\n",
    "        (1-alpha) * A * K_L**alpha * N**(-alpha) - W, # MPN\n",
    "        (1 - delta) * K_L + I_L - K, # Aggregate resource constraint \n",
    "        # Do not use (1 - delta) * K + I - K_P\n",
    "        Y - C - I, # Aggregate resource constraint\n",
    "        rho * np.log(A_L) + epsilon - np.log(A) # TFP evolution\n",
    "        ))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64ca769e-dadd-460b-9364-367bc1ccca11",
   "metadata": {},
   "source": [
    "To calculate power result, we use ^ in MATLAB, and use ** in Python.\n",
    "\n",
    "And to calculate log, we directly use log in MATLAB, and use np.log in Python."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5f3f85e-943d-4a2c-9eb2-23e951a81ac1",
   "metadata": {},
   "source": [
    "__def F(X_Lag, X, X_Prime, epsilon)__: Defines a function F that contains the model equations.\n",
    "\n",
    "Returns a stacked array with the 8 equations we discussed above.\n",
    "   \n",
    "__numpy.hstack()__ function is used to stack the sequence of input arrays horizontally (i.e. column wise) to make a single array."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86998079-34e1-4cce-b587-f7fb32a0b93d",
   "metadata": {},
   "source": [
    "## Check steady state"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00ed968f-d11d-4772-8bfa-60131803be5b",
   "metadata": {},
   "source": [
    "When we select a proper $\\phi$, we should get an array consists of zeros, or close to zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb8321e7-5f03-4fc3-947a-aa541b60b0b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.    , 0.0025, 0.    , 0.    , 0.    , 0.    , 0.    , 0.    ])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " F(X_SS,X_SS,X_SS,epsilon_SS)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2f03998-931d-493a-ac85-79606d6e3b2e",
   "metadata": {},
   "source": [
    "## Linearize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27fbb242-8421-4846-997d-a14df12f17c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A: [[ 0.      0.      0.      0.      0.     -1.1584  0.      0.    ]\n",
      " [ 0.      0.      0.      0.      0.      0.      0.      0.    ]\n",
      " [ 0.      0.      0.      0.      0.      0.      0.      0.    ]\n",
      " [ 0.      0.      0.      0.      0.      0.      0.      0.    ]\n",
      " [ 0.      0.      0.      0.      0.      0.      0.      0.    ]\n",
      " [ 0.      0.      0.      0.      0.      0.      0.      0.    ]\n",
      " [ 0.      0.      0.      0.      0.      0.      0.      0.    ]\n",
      " [ 0.      0.      0.      0.      0.      0.      0.      0.    ]]\n",
      "B: [[ 0.      0.99    0.      0.      0.      1.1584  0.      0.    ]\n",
      " [ 0.      0.      7.8     0.      0.      3.0089  0.     -1.1584]\n",
      " [ 1.1499  0.      2.2423  0.     -1.      0.      0.      0.    ]\n",
      " [ 0.0351 -1.      0.0684  0.      0.      0.      0.      0.    ]\n",
      " [ 2.2423  0.     -2.3545  0.      0.      0.      0.     -1.    ]\n",
      " [ 0.      0.      0.     -1.      0.      0.      0.      0.    ]\n",
      " [ 0.      0.      0.      0.      1.     -1.     -1.      0.    ]\n",
      " [-1.      0.      0.      0.      0.      0.      0.      0.    ]]\n",
      "C: [[ 0.      0.      0.      0.      0.      0.      0.      0.    ]\n",
      " [ 0.      0.      0.      0.      0.      0.      0.      0.    ]\n",
      " [ 0.      0.      0.      0.0351  0.      0.      0.      0.    ]\n",
      " [ 0.      0.      0.     -0.002   0.      0.      0.      0.    ]\n",
      " [ 0.      0.      0.      0.0684  0.      0.      0.      0.    ]\n",
      " [ 0.      0.      0.      0.975   0.      0.      1.      0.    ]\n",
      " [ 0.      0.      0.      0.      0.      0.      0.      0.    ]\n",
      " [ 0.95    0.      0.      0.      0.      0.      0.      0.    ]]\n",
      "E: [0. 0. 0. 0. 0. 0. 0. 1.]\n"
     ]
    }
   ],
   "source": [
    "A = jacobian(lambda x: F(X_SS,X_SS,x,epsilon_SS))(X_SS)\n",
    "B = jacobian(lambda x: F(X_SS,x,X_SS,epsilon_SS))(X_SS)\n",
    "C = jacobian(lambda x: F(x,X_SS,X_SS,epsilon_SS))(X_SS)\n",
    "E = jacobian(lambda x: F(X_SS,X_SS,X_SS,x))(epsilon_SS)\n",
    "\n",
    "print(\"A: {}\".format(A))\n",
    "print(\"B: {}\".format(B))\n",
    "print(\"C: {}\".format(C))\n",
    "print(\"E: {}\".format(E))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a35a3bfe-cbf2-4445-bf42-fcfa16e12067",
   "metadata": {},
   "source": [
    "Calculate the Jacobian matrices for the model equations, which represent how the system responds to small changes in state variables.\n",
    "\n",
    "The last __(X_SS)__ means evaluating the Jacobian at the point X_SS."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28dd2d51-f0c6-4b4f-be9e-e3402acc056e",
   "metadata": {},
   "source": [
    "A: How small change of future variables affect the system equilibrium, starting ananlysis from X_SS.\n",
    "\n",
    "B: How nowadays affect the system.\n",
    "\n",
    "C: How past affect the system.\n",
    "\n",
    "E: How exogenous shock affect the system, starting analysis from epsilon_SS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75828601-7d16-42d1-913b-cb245b47aaa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def SolveSystem(A,B,C,E):\n",
    "    # Solve the system using linear time iteration as in Rendahl (2017)    \n",
    "    MAXIT = 1000    \n",
    "    P = np.zeros(A.shape)  \n",
    "\n",
    "    for it in range(MAXIT):\n",
    "        P = -np.linalg.lstsq(B+A@P,C,rcond=None)[0]   # (B+A⋅P)⋅(-P)=C        \n",
    "        test = np.max(np.abs(C+B@P+A@P@P))        # C+B*P+A*P*P=0\n",
    "        if test < 1e-7:\n",
    "            break\n",
    "\n",
    "    # Impact matrix\n",
    "    #  Solution is x_{t}=P*x_{t-1}+Q*eps_t\n",
    "    Q = -np.linalg.inv(B+A@P) @ E    # Q = - E*(B+AP)\n",
    "\n",
    "    return P, Q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6bfc7ab8-c1bd-48a6-a72f-cd56ab52d6eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.95   -0.     -0.     -0.     -0.     -0.     -0.     -0.    ]\n",
      " [ 0.0444 -0.     -0.     -0.0023 -0.     -0.     -0.0008 -0.    ]\n",
      " [ 0.1614 -0.     -0.     -0.0052 -0.     -0.     -0.0121 -0.    ]\n",
      " [ 0.     -0.     -0.      0.975  -0.     -0.      1.     -0.    ]\n",
      " [ 1.4543 -0.     -0.      0.0235 -0.     -0.     -0.0271 -0.    ]\n",
      " [ 0.2554 -0.     -0.      0.0445 -0.     -0.      0.0423 -0.    ]\n",
      " [ 1.1989 -0.     -0.     -0.0211 -0.     -0.     -0.0694 -0.    ]\n",
      " [ 1.7502 -0.     -0.      0.0807 -0.     -0.      0.0285 -0.    ]]\n",
      "[1.     0.0467 0.1699 0.     1.5309 0.2689 1.262  1.8423]\n"
     ]
    }
   ],
   "source": [
    "P, Q = SolveSystem(A,B,C,E)\n",
    "print(P)\n",
    "print(Q)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86553e2d-7869-4f91-a771-734b32cd4e13",
   "metadata": {},
   "source": [
    "Calls a function __SolveSystem__ to solve the linearized system of equations, returning matrices P and Q."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3d48b9e-1898-4e7d-93e4-e4173d912dab",
   "metadata": {},
   "source": [
    "## Calculate an impulse response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fdde39e-a9a4-41c1-b688-839885717c36",
   "metadata": {},
   "outputs": [],
   "source": [
    "IRF_RBC = np.zeros((nX,100))\n",
    "IRF_RBC[:,0] = Q * 0.01\n",
    "\n",
    "for t in range(1,100):\n",
    "    IRF_RBC[:,t] = P@IRF_RBC[:,t-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa951fd6-c2bb-4456-9e86-be0aac0f6bb6",
   "metadata": {},
   "source": [
    "__IRF_RBC = np.zeros((nX, 100))__: Initializes a matrix to store impulse response functions.\n",
    "\n",
    "__IRF_RBC[:, 0] = Q * 0.01__: Sets the initial impulse response based on matrix Q.\n",
    "\n",
    "Loop: Iteratively calculates the impulse responses for 100 periods using the transition matrix P.\n",
    "\n",
    "In Python, the @ operator is used for matrix multiplication. To convert this line to MATLAB, you will use the * operator instead"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80d6df68-6daa-4cbf-899a-562c48e37749",
   "metadata": {},
   "source": [
    "## Print"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0586098-e671-49c7-9625-ae001a5dfbcf",
   "metadata": {},
   "source": [
    "Finally, we plot the impulse response function, which is the same as what we get in MATLAB and Dynare."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24582363-e971-4fb7-8295-40d6d7803ab7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# create a 3x3 subplot\n",
    "fig, axs = plt.subplots(3, 3, figsize=(10, 10))\n",
    "\n",
    "# subplot\n",
    "axs[0, 0].plot(IRF_RBC[iA, :], label='Impulse Response of Output', color='blue')\n",
    "axs[0, 0].set_title('TFP shock')\n",
    "axs[0, 0].axhline(0, color='black', lw=0.5, ls='--') \n",
    "# axs[0, 0].legend()\n",
    "\n",
    "axs[0, 1].plot(IRF_RBC[iY, :], label='Impulse Response of Output', color='blue')\n",
    "axs[0, 1].set_title('Output')\n",
    "axs[0, 1].axhline(0, color='black', lw=0.5, ls='--') \n",
    "\n",
    "axs[0, 2].plot(IRF_RBC[iC, :], label='Impulse Response of Output', color='blue')\n",
    "axs[0, 2].set_title('Consumption')\n",
    "axs[0, 2].axhline(0, color='black', lw=0.5, ls='--') \n",
    "\n",
    "axs[1, 0].plot(IRF_RBC[iK, :], label='Impulse Response of Output', color='blue')\n",
    "axs[1, 0].set_title('Capital')\n",
    "axs[1, 0].axhline(0, color='black', lw=0.5, ls='--') \n",
    "\n",
    "axs[1, 1].plot(IRF_RBC[iN, :], label='Impulse Response of Output', color='blue')\n",
    "axs[1, 1].set_title('Labor')\n",
    "axs[1, 1].axhline(0, color='black', lw=0.5, ls='--') \n",
    "\n",
    "axs[1, 2].plot(IRF_RBC[iW, :], label='Impulse Response of Output', color='blue')\n",
    "axs[1, 2].set_title('Wage')\n",
    "axs[1, 2].axhline(0, color='black', lw=0.5, ls='--') \n",
    "\n",
    "axs[2, 0].plot(IRF_RBC[iI, :], label='Impulse Response of Output', color='blue')\n",
    "axs[2, 0].set_title('Investment')\n",
    "axs[2, 0].axhline(0, color='black', lw=0.5, ls='--') \n",
    "\n",
    "axs[2, 1].plot(IRF_RBC[iR, :], label='Impulse Response of Output', color='blue')\n",
    "axs[2, 1].set_title('Interest Rate')\n",
    "axs[2, 1].axhline(0, color='black', lw=0.5, ls='--') \n",
    "\n",
    "axs[2, 2].axis('off')\n",
    "\n",
    "# layout adjustment \n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
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