{ "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", "%matplotlib inline" ] }, { "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 = cur.a**p.rhoa- 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", " n_states=2,\n", " n_exo_states=1,\n", " var_names=['a','k','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": { "scrolled": false }, "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 `.log_linear()` method to find the log-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 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 their steady state values." ] }, { "cell_type": "code", "execution_count": 7, "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": [ "Finally, we need to obtain the *solution* to the log-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", " [[ 0.2297 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": 9, "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 0.0 0.000 0.000000 0.000000 0.000000 0.000000\n", "1 1.0 1.000 0.000000 0.229718 1.000000 3.319739\n", "2 0.0 0.900 0.082993 0.249318 0.929048 2.976083\n", "3 0.0 0.810 0.155321 0.265744 0.864362 2.667127\n", "4 0.0 0.729 0.218116 0.279348 0.805341 2.389390\n" ] } ], "source": [ "# Compute impulse responses and plot\n", "rbc.impulse(T=41,t0=1,shocks=None,percent=True)\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": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "rbc.irs['e_a'][['a','k','c','y','i']].plot(lw='5',alpha=0.5,grid=True).legend(loc='upper right',ncol=2)\n", "rbc.irs['e_a'][['e_a','a']].plot(lw='5',alpha=0.5,grid=True).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 0." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "rbc.stoch_sim(T=121,drop_first=100,cov_mat=np.array([0.00763**2]),seed=0,percent=True)\n", "rbc.simulated[['k','c','y','i']].plot(lw='5',alpha=0.5,grid=True).legend(loc='upper right',ncol=4)\n", "rbc.simulated[['a']].plot(lw='5',alpha=0.5,grid=True).legend(ncol=4)\n", "rbc.simulated['e_a'].plot(lw='5',alpha=0.5,grid=True).legend(ncol=4)" ] } ], "metadata": { "anaconda-cloud": {}, "celltoolbar": "Raw Cell Format", "kernelspec": { "display_name": "Python 3", "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 1 }