{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# A Detailed RBC Model Example\n", "\n", "Consider the equilibrium conditions for a basic RBC model without labor:\n", "\n", "\\begin{align}\n", "C_t^{-\\sigma} & = \\beta E_t \\left[C_{t+1}^{-\\sigma}(\\alpha A_{t+1} K_{t+1}^{\\alpha-1} + 1 - \\delta)\\right]\\\\\n", "Y_t & = A_t K_t^{\\alpha}\\\\\n", "I_t & = K_{t+1} - (1-\\delta)K_t\\\\\n", "Y_t & = C_t + I_t\\\\\n", "\\log A_t & = \\rho_a \\log A_{t-1} + \\epsilon_t\n", "\\end{align}\n", "\n", "In the nonstochastic steady state, we have:\n", "\n", "\\begin{align}\n", "K & = \\left(\\frac{\\alpha A}{1/\\beta+\\delta-1}\\right)^{\\frac{1}{1-\\alpha}}\\\\\n", "Y & = AK^{\\alpha}\\\\\n", "I & = \\delta K\\\\\n", "C & = Y - I\n", "\\end{align}\n", "\n", "Given values for the parameters $\\beta$, $\\sigma$, $\\alpha$, $\\delta$, and $A$, steady state values of capital, output, investment, and consumption are easily computed.\n", "\n", "## Import requisite modules" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Import numpy, pandas, linearsolve, matplotlib.pyplot\n", "import numpy as np\n", "import pandas as pd\n", "import linearsolve as ls\n", "import matplotlib.pyplot as plt\n", "plt.style.use('classic')\n", "plt.rcParams['figure.facecolor'] = 'white'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Initializing the model in `linearsolve`\n", "\n", "To initialize the model, we need to first set the model's parameters. We do this by creating a Pandas Series variable called `parameters`:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Input model parameters\n", "parameters = pd.Series(dtype=float)\n", "parameters['alpha'] = .35\n", "parameters['beta'] = 0.99\n", "parameters['delta'] = 0.025\n", "parameters['rhoa'] = .9\n", "parameters['sigma'] = 1.5\n", "parameters['A'] = 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we need to define a function that returns the equilibrium conditions of the model. The function will take as inputs two vectors: one vector of \"current\" variables and another of \"forward-looking\" or one-period-ahead variables. The function will return an array that represents the equilibirum conditions of the model. We'll enter each equation with all variables moved to one side of the equals sign. For example, here's how we'll enter the produciton fucntion:\n", "\n", "`production_function = technology_current*capital_current**alpha - output_curent`\n", "\n", "Here the variable `production_function` stores the production function equation set equal to zero. We can enter the equations in almost any way we want. For example, we could also have entered the production function this way:\n", "\n", "`production_function = 1 - output_curent/technology_current/capital_current**alpha`\n", "\n", "One more thing to consider: the natural log in the equation describing the evolution of total factor productivity will create problems for the solution routine later on. So rewrite the equation as:\n", "\n", "\\begin{align}\n", "A_{t+1} & = A_{t}^{\\rho_a}e^{\\epsilon_{t+1}}\\\\\n", "\\end{align}\n", "\n", "So the complete system of equations that we enter into the program looks like:\n", "\n", "\\begin{align}\n", "C_t^{-\\sigma} & = \\beta E_t \\left[C_{t+1}^{-\\sigma}(\\alpha Y_{t+1} /K_{t+1}+ 1 - \\delta)\\right]\\\\\n", "Y_t & = A_t K_t^{\\alpha}\\\\\n", "I_t & = K_{t+1} - (1-\\delta)K_t\\\\\n", "Y_t & = C_t + I_t\\\\\n", "A_{t+1} & = A_{t}^{\\rho_a}e^{\\epsilon_{t+1}}\n", "\\end{align}\n", "\n", "Now let's define the function that returns the equilibrium conditions:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Define function to compute equilibrium conditions\n", "def equations(variables_forward,variables_current,parameters):\n", " \n", " # Parameters \n", " p = parameters\n", " \n", " # Variables\n", " fwd = variables_forward\n", " cur = variables_current\n", "\n", " # Household Euler equation\n", " euler_eqn = p.beta*fwd.c**-p.sigma*(p.alpha*fwd.y/fwd.k+1-p.delta) - cur.c**-p.sigma\n", " \n", " # Production function\n", " production_fuction = cur.a*cur.k**p.alpha - cur.y\n", " \n", " # Capital evolution\n", " capital_evolution = fwd.k - (1-p.delta)*cur.k - cur.i\n", " \n", " # Goods market clearing\n", " market_clearing = cur.c + cur.i - cur.y\n", " \n", " # Exogenous technology\n", " technology_proc = p.rhoa*np.log(cur.a)- np.log(fwd.a)\n", " \n", " # Stack equilibrium conditions into a numpy array\n", " return np.array([\n", " euler_eqn,\n", " production_fuction,\n", " capital_evolution,\n", " market_clearing,\n", " technology_proc\n", " ])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that inside the function we have to define the variables of the model form the elements of the input vectors `variables_forward` and `variables_current`.\n", "\n", "## Initializing the model\n", "\n", "To initialize the model, we need to specify the total number of state variables in the model, the number of state variables with exogenous shocks, the names of the endogenous variables, and the parameters of the model. \n", "\n", "It is *essential* that the variable names are ordered in the following way: First the names of the endogenous variables with the state variables with exogenous shocks, then the state variables without shocks, and finally the control variables. Ordering within the groups doesn't matter." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Initialize the model\n", "rbc = ls.model(equations = equations,\n", " exo_states = 'a',\n", " endo_states = 'k',\n", " costates = ['c','y','i'],\n", " parameters=parameters)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Steady state\n", "\n", "Next, we need to compute the nonstochastic steady state of the model. The `.compute_ss()` method can be used to compute the steady state numerically. The method's default is to use scipy's `fsolve()` function, but other scipy root-finding functions can be used: `root`, `broyden1`, and `broyden2`. The optional argument `options` lets the user pass keywords directly to the optimization function. Check out the documentation for Scipy's nonlinear solvers here: http://docs.scipy.org/doc/scipy/reference/optimize.html" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a 1.000000\n", "k 34.398226\n", "c 2.589794\n", "y 3.449750\n", "i 0.859956\n", "dtype: float64\n" ] } ], "source": [ "# Compute the steady state numerically\n", "guess = [1,1,1,1,1]\n", "rbc.compute_ss(guess)\n", "print(rbc.ss)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the steady state is returned as a Pandas Series. Alternatively, you could compute the steady state directly and then sent the `rbc.ss` attribute:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a 1.000000\n", "k 34.398226\n", "c 2.589794\n", "y 3.449750\n", "i 0.859956\n", "dtype: float64\n" ] } ], "source": [ "# Steady state solution\n", "p = parameters\n", "K = (p.alpha*p.A/(1/p.beta+p.delta-1))**(1/(1-p.alpha))\n", "C = p.A*K**p.alpha - p.delta*K\n", "Y = p.A*K**p.alpha\n", "I = Y - C\n", "\n", "rbc.set_ss([p.A,K,C,Y,I])\n", "print(rbc.ss)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Log-linearization and solution\n", "\n", "Now we use the `.linear_approximation()` method to find the linear appxoximation to the model's equilibrium conditions. That is, we'll transform the nonlinear model into a linear model in which all variables are expressed as differences from the steady state. Specifically, we'll compute the matrices $A$ and $B$ that satisfy:\n", "\n", "\\begin{align}\n", "A E_t\\left[ x_{t+1} \\right] & = B x_t + \\left[ \\begin{array}{c} \\epsilon_{t+1} \\\\ 0 \\end{array} \\right],\n", "\\end{align}\n", "\n", "where the vector $x_{t}$ denotes the difference of the endogenous variables from their steady state values." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The matrix A:\n", "\n", " [[ 0.00e+00 -2.00e-04 -1.39e-01 2.40e-03 0.00e+00]\n", " [ 0.00e+00 0.00e+00 0.00e+00 0.00e+00 0.00e+00]\n", " [ 0.00e+00 1.00e+00 0.00e+00 0.00e+00 0.00e+00]\n", " [ 0.00e+00 0.00e+00 0.00e+00 0.00e+00 0.00e+00]\n", " [-1.00e+00 0.00e+00 0.00e+00 0.00e+00 0.00e+00]] \n", "\n", "\n", "The matrix B:\n", "\n", " [[-0. -0. -0.139 -0. -0. ]\n", " [-3.4497 -0.0351 -0. 1. -0. ]\n", " [-0. 0.975 -0. -0. 1. ]\n", " [-0. -0. -1. 1. -1. ]\n", " [-0.9 -0. -0. -0. -0. ]]\n" ] } ], "source": [ "# Find the linear approximation around the non-stochastic steady state\n", "rbc.linear_approximation()\n", "\n", "print('The matrix A:\\n\\n',np.around(rbc.a,4),'\\n\\n')\n", "print('The matrix B:\\n\\n',np.around(rbc.b,4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we need to obtain the *solution* to the linearized model. The solution is a pair of matrices $F$ and $P$ that specify:\n", "\n", "1. The current values of the non-state variables $u_{t}$ as a linear function of the previous values of the state variables $s_t$.\n", "1. The future values of the state variables $s_{t+1}$ as a linear function of the previous values of the state variables $s_t$ and the future realisation of the exogenous shock process $\\epsilon_{t+1}$.\n", "\n", "\\begin{align}\n", "u_t & = Fs_t\\\\\n", "s_{t+1} & = Ps_t + \\epsilon_{t+1}.\n", "\\end{align}\n", "\n", "We use the `.klein()` method to find the solution." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The matrix F:\n", "\n", " [[ 0.5949 0.0386]\n", " [ 3.4497 0.0351]\n", " [ 2.8548 -0.0035]] \n", "\n", "\n", "The matrix P:\n", "\n", " [[0.9 0. ]\n", " [2.8548 0.9715]]\n" ] } ], "source": [ "# Solve the model\n", "rbc.solve_klein(rbc.a,rbc.b)\n", "\n", "# Display the output\n", "print('The matrix F:\\n\\n',np.around(rbc.f,4),'\\n\\n')\n", "print('The matrix P:\\n\\n',np.around(rbc.p,4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Log-linearization and solution\n", "\n", "Now we use the `.log_linear_approximation()` method to find the log-linear appxoximation to the model's equilibrium conditions. We will transform the nonlinear model into a log-linear model in which all variables are expressed as log-deviations from the steady state. Specifically, we'll compute the matrices $A$ and $B$ that satisfy:\n", "\n", "\\begin{align}\n", "A E_t\\left[ x_{t+1} \\right] & = B x_t + \\left[ \\begin{array}{c} \\epsilon_{t+1} \\\\ 0 \\end{array} \\right],\n", "\\end{align}\n", "\n", "where the vector $x_{t}$ denotes the log deviation of the endogenous variables from the steady state. Note the next cell will overwrite the matrics $A$ and $B$ computed by the linear approximation above." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The matrix A:\n", "\n", " [[ 0.00000e+00 -8.30000e-03 -3.59900e-01 8.30000e-03 0.00000e+00]\n", " [ 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00]\n", " [ 0.00000e+00 3.43982e+01 0.00000e+00 0.00000e+00 0.00000e+00]\n", " [ 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00]\n", " [-1.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00]] \n", "\n", "\n", "The matrix B:\n", "\n", " [[-0. -0. -0.3599 -0. -0. ]\n", " [-3.4497 -1.2074 -0. 3.4497 -0. ]\n", " [-0. 33.5383 -0. -0. 0.86 ]\n", " [-0. -0. -2.5898 3.4497 -0.86 ]\n", " [-0.9 -0. -0. -0. -0. ]]\n" ] } ], "source": [ "# Find the log-linear approximation around the non-stochastic steady state\n", "rbc.log_linear_approximation()\n", "\n", "print('The matrix A:\\n\\n',np.around(rbc.a,4),'\\n\\n')\n", "print('The matrix B:\\n\\n',np.around(rbc.b,4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next find the *solution* to the log-linearized model:\n", "\n", "\\begin{align}\n", "u_t & = Fs_t\\\\\n", "s_{t+1} & = Ps_t + \\epsilon_{t+1}.\n", "\\end{align}\n", "\n", "We use the `.klein()` method to find the solution. Note the next cell will overwrite the matrics $F$ and $P$ computed by the solution to the linear approximation above." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The matrix F:\n", "\n", " [[ 0.2297 0.513 ]\n", " [ 1. 0.35 ]\n", " [ 3.3197 -0.1408]] \n", "\n", "\n", "The matrix P:\n", "\n", " [[0.9 0. ]\n", " [0.083 0.9715]]\n" ] } ], "source": [ "# Solve the model\n", "rbc.solve_klein(rbc.a,rbc.b)\n", "\n", "# Display the output\n", "print('The matrix F:\\n\\n',np.around(rbc.f,4),'\\n\\n')\n", "print('The matrix P:\\n\\n',np.around(rbc.p,4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Impulse responses\n", "\n", "One the model is solved, use the `.impulse()` method to compute impulse responses to exogenous shocks to the state. The method creates the `.irs` attribute which is a dictionary with keys equal to the names of the exogenous shocks and the values are Pandas DataFrames with the computed impulse respones. You can supply your own values for the shocks, but the default is 0.01 for each exogenous shock." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Impulse responses to a 0.01 unit shock to A:\n", "\n", " e_a a k c y i\n", "0 0.00 0.00000 0.000000 0.000000 0.000000 0.000000\n", "1 0.01 0.01000 0.000000 0.002297 0.010000 0.033197\n", "2 0.00 0.00900 0.000830 0.002493 0.009290 0.029761\n", "3 0.00 0.00810 0.001553 0.002657 0.008644 0.026671\n", "4 0.00 0.00729 0.002181 0.002793 0.008053 0.023894\n" ] } ], "source": [ "# Compute impulse responses and plot\n", "rbc.impulse(T=41,t0=1,shocks={'e_a':0.01})\n", "\n", "print('Impulse responses to a 0.01 unit shock to A:\\n\\n',rbc.irs['e_a'].head())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plotting is easy." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(12,4))\n", "ax = fig.add_subplot(1,2,1)\n", "(rbc.irs['e_a'][['a','k','c','y','i']]*100).plot(lw='5',alpha=0.5,grid=True,ax=ax,ylabel='Percent').legend(loc='upper right',ncol=2)\n", "# ax.set_ylabel=\n", "ax = fig.add_subplot(1,2,2)\n", "(rbc.irs['e_a'][['a','e_a']]*100).plot(lw='5',alpha=0.5,grid=True,ax=ax,ylabel='Percent').legend(loc='upper right',ncol=2);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Stochastic simulation\n", "\n", "Creating a stochastic simulation of the model is straightforward with the `.stoch_sim()` method. In the following example, I create a 151 period (including t=0) simulation by first simulating the model for 251 periods and then dropping the first 100 values. The standard deviation of the shock to $A_t$ is set to 0.00763. The seed for the numpy random number generator is set to 138431." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rbc.stoch_sim(T=121,drop_first=100,covariance_matrix=[0.00763],seed=138431)\n", "\n", "fig = plt.figure(figsize=(12,8))\n", "\n", "ax = fig.add_subplot(2,1,1)\n", "(rbc.simulated[['a','k','c','y','i']]*100).plot(lw='5',alpha=0.5,grid=True,ax=ax,ylabel='Percent',title='Endogenous variables').legend(loc='upper right',ncol=5)\n", "\n", "ax = fig.add_subplot(2,1,2)\n", "(rbc.simulated['e_a']*100).plot(lw='5',alpha=0.5,grid=True,title='TFP shock',ylabel='Percent');" ] } ], "metadata": { "anaconda-cloud": {}, "celltoolbar": "Raw Cell Format", "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 4 }