{ "cells": [ { "cell_type": "markdown", "source": [ "# Replication of Mackowiak, Matejka and Wiederholt (2018)" ], "metadata": {} }, { "cell_type": "markdown", "source": [ "This example replicates [Mackowiak, Matejka and Wiederholt (2018)](https://www.sciencedirect.com/science/article/abs/pii/S002205311830139X) using the [DRIPs](https://github.com/afrouzi/DRIPs.jl) package." ], "metadata": {} }, { "cell_type": "markdown", "source": [ "See [Afrouzi and Yang (2020)](http://www.afrouzi.com/dynamic_inattention.pdf) for background on the theory." ], "metadata": {} }, { "cell_type": "markdown", "source": [ "## Contents\n", "* Ex. 1: AR(2) Process\n", " * Initialization\n", " * Replication of Figures (2) and (3)\n", " * Measure Performance\n", "* Ex. 2: AR(3) and ARMA(2,1) Processes\n", " * Solve and Measure Performance\n", " * Replication of Figure (5)\n", "* Ex. 3: Price-setting with Rational Inattention\n", " * The Case with No Strategic Complementarity\n", " * The Case with Strategic Complementarity\n", " * Replication of Figure (6)\n", "* Ex. 4: Business Cycle Model with News Shocks\n", " * Setup\n", " * Initialization\n", " * Solve and Measure Performance\n", " * Replication of Figure (7)" ], "metadata": {} }, { "cell_type": "markdown", "source": [ "## Ex. 1: AR(2) Process\n", "In this example, authors assume that the optimal action follows an AR(2) process,\n", "$$\n", "\\begin{aligned}\n", " x_t = \\phi_1 x_{t-1} + \\phi_2 x_{t-2} + \\theta_0 \\varepsilon_t\n", "\\end{aligned}\n", "$$\n", "Then, we can write a state-space form as:\n", "$$\n", "\\begin{aligned}\n", " \\left[\\begin{array}{c} x_t \\\\ x_{t-1} \\end{array}\\right]\n", " & =\n", " \\underset{\\mathbf{A}}{\\underbrace{\\left[\\begin{array}{cc}\n", " \\phi_1 & \\phi_2 \\\\\n", " 1 & 0\\end{array}\\right]}}\\,\n", " \\left[\\begin{array}{c} x_{t-1} \\\\ x_{t-2} \\end{array}\\right]\n", " + \\underset{\\mathbf{Q}}{\\underbrace{\\left[\\begin{array}{c}\n", " \\theta_0 \\\\\n", " 0\\end{array}\\right]}}\\, \\varepsilon_t\n", "\\end{aligned}\n", "$$\n", "We now characterize the optimal signal, $S_{t} = h_1 X_t + h_2 X_{t-1} + \\psi_t$, as a function of $\\phi_2$ and the capacity for information processing ($\\kappa$)." ], "metadata": {} }, { "cell_type": "markdown", "source": [ "### Initialization\n", "Include the package:" ], "metadata": {} }, { "outputs": [], "cell_type": "code", "source": [ "using DRIPs, LinearAlgebra;" ], "metadata": {}, "execution_count": 1 }, { "cell_type": "markdown", "source": [ "Assign value to deep parameters and define the structure of the problem" ], "metadata": {} }, { "outputs": [], "cell_type": "code", "source": [ "β = 1.0 ; # Time preference\n", "θ0 = 1.0 ;\n", "\n", "ϕ2_seq = 0:0.02:0.9 ; # sequence of values for the AR(2) parameter\n", "n_ϕ = length(ϕ2_seq) ;\n", "\n", "κ_seq = 0.008:0.01:3.5 ; # sequence of values for the information processing capacity\n", "n_κ = length(κ_seq) ;\n", "\n", "H = [1; 0] ;\n", "Q = [θ0 ; 0] ;" ], "metadata": {}, "execution_count": 2 }, { "cell_type": "markdown", "source": [ "### Replication of Figures (2) and (3)\n", "Solve for different values of $\\phi_2$:" ], "metadata": {} }, { "outputs": [], "cell_type": "code", "source": [ "κ = 0.2 ; # fix κ for this exercise so we can vary ϕ2\n", "\n", "h1 = zeros(n_ϕ,1) ;\n", "h2 = zeros(n_ϕ,1) ;\n", "h2norm = zeros(n_ϕ,1) ;\n", "\n", "for i in 1:n_ϕ\n", " ϕ2 = ϕ2_seq[i] ;\n", " ϕ1 = 0.9 - ϕ2 ;\n", " A = [ϕ1 ϕ2 ; 1.0 0.0] ;\n", "\n", " ex1a = Drip(κ,β,A,Q,H,fcap=true);\n", "\n", " h1[i] = ex1a.ss.Y[1]\n", " h2[i] = ex1a.ss.Y[2]\n", " h2norm[i] = ex1a.ss.Y[2]/ex1a.ss.Y[1] # normalize the first signal weight h1 to one\n", "end" ], "metadata": {}, "execution_count": 3 }, { "cell_type": "markdown", "source": [ "Here is the replication of Figure (2):" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "Plot{Plots.PyPlotBackend() n=1}", "image/png": 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normalized to 1)\",\n", " legend = :topleft,\n", " lw = 2,\n", " color = :black,\n", " xlim = (0,0.9),\n", " xticks = (0:0.1:0.9),\n", " titlefont = font(10), legendfont = font(7), tickfont = font(7),\n", " size = (650,300),\n", " grid = :off, framestyle = :box)" ], "metadata": {}, "execution_count": 4 }, { "cell_type": "markdown", "source": [ "Now, we solve for the signal weight and standard deviation of noise, for different values of $\\kappa$" ], "metadata": {} }, { "outputs": [], "cell_type": "code", "source": [ "ϕ2 = 0.4 ; # fix ϕ2 in this exercise so we can vary κ\n", "ϕ1 = 0.99 - ϕ2 ;\n", "\n", "A = [ϕ1 ϕ2 ; 1.0 0.0] ;\n", "\n", "ω_sol = zeros(n_κ,1) ;\n", "σz_sol = zeros(n_κ,1) ;\n", "\n", "for i in 1:n_κ\n", " κ = κ_seq[i] ;\n", "\n", " ex1b = Drip(κ,β,A,Q,H,fcap=true);\n", "\n", " h1temp = ex1b.ss.Y[1]\n", " h2temp = ex1b.ss.Y[2]\n", "\n", " ω_sol[i] = 1.0 - (h2temp/h1temp)/(ϕ2 + (1.0-ϕ1)*(h2temp/h1temp)) ;\n", " ρ = (ω_sol[i] + (1.0-ω_sol[i])*ϕ1)/h1temp\n", " σz_sol[i] = ρ*ex1b.ss.Σ_z[1,1] ;\n", "end" ], "metadata": {}, "execution_count": 5 }, { "cell_type": "markdown", "source": [ "Below is the replication of Figure (3):" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "Plot{Plots.PyPlotBackend() n=3}", "image/png": 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\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n" ] }, "metadata": {}, "execution_count": 6 } ], "cell_type": "code", "source": [ "a = fill(NaN, n_κ, 1)\n", "\n", "plot(κ_seq,[ω_sol[:,1] a],\n", " xlabel = L\"$\\kappa$\",\n", " label = [L\"Signal weight on $X_{t}$, $\\omega$ (left axis)\" L\"St. dev. of noise $\\sigma_{\\psi}$ (right axis)\"],\n", " title = L\"Optimal signal as function of $\\kappa$, AR(2) example.\",\n", " linestyle = [:solid :dash], color = [:black :gray40],\n", " xticks = (0:1:3),\n", " xlim = (-0.02,3.02),\n", " yticks = (0:0.5:1),\n", " ylim = (-0.01,1.01),\n", " lw = 2, grid = :off, legend = :right,\n", " titlefont = font(10), legendfont = font(8), tickfont = font(8),\n", " framestyle = :box)\n", "plot!(twinx(),κ_seq,σz_sol[:,1],\n", " linestyle = :dash, color = :gray40,\n", " label = \"\",\n", " xlim = (-0.02,3.02),\n", " xticks = false,\n", " yticks = (0:6:12),\n", " ylim = (-0.12,12.12),\n", " lw = 2, tickfont = font(7),\n", " size = (650,300),\n", " grid = :off, framestyle = :box)" ], "metadata": {}, "execution_count": 6 }, { "cell_type": "markdown", "source": [ "### Measure Performance\n", "Benchmark the solution for random values of $\\phi_2$:" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "BenchmarkTools.Trial: \n memory estimate: 431.52 KiB\n allocs estimate: 4150\n --------------\n minimum time: 214.093 μs (0.00% GC)\n median time: 288.728 μs (0.00% GC)\n mean time: 321.188 μs (11.65% GC)\n maximum time: 16.050 ms (97.58% GC)\n --------------\n samples: 10000\n evals/sample: 1" }, "metadata": {}, "execution_count": 7 } ], "cell_type": "code", "source": [ "using BenchmarkTools;\n", "@benchmark Drip(κ,β,[0.9-ϕ2 ϕ2; 1.0 0.0],Q,H,fcap = true) setup = (ϕ2 = 0.9*rand())" ], "metadata": {}, "execution_count": 7 }, { "cell_type": "markdown", "source": [ "Benchmark the solution for random values of $κ$:" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "BenchmarkTools.Trial: \n memory estimate: 74.66 KiB\n allocs estimate: 729\n --------------\n minimum time: 41.237 μs (0.00% GC)\n median time: 135.851 μs (0.00% GC)\n mean time: 227.236 μs (10.97% GC)\n maximum time: 16.126 ms (96.27% GC)\n --------------\n samples: 10000\n evals/sample: 1" }, "metadata": {}, "execution_count": 8 } ], "cell_type": "code", "source": [ "@benchmark Drip(κ,β,[0.5 0.4; 1.0 0.0],Q,H,fcap = true) setup = (κ = rand())" ], "metadata": {}, "execution_count": 8 }, { "cell_type": "markdown", "source": [ "## Ex. 2: AR(3) and ARMA(2,1) Processes" ], "metadata": {} }, { "cell_type": "markdown", "source": [ "Here, we replicate the AR(3) and ARMA(2,1) examples in Mackowiak, Matejka and Wiederholt (2018).\n", "Using a similar method to the AR(2) case, we can write the law of motion of optimal actions as a state-space form." ], "metadata": {} }, { "cell_type": "markdown", "source": [ "### Solve and Meausre Performance" ], "metadata": {} }, { "cell_type": "markdown", "source": [ "#### The AR(3) Example" ], "metadata": {} }, { "cell_type": "markdown", "source": [ "Initialize:" ], "metadata": {} }, { "outputs": [], "cell_type": "code", "source": [ "β = 1.0 ;\n", "θ0 = 1.0 ;\n", "κ = 10.8 ;\n", "\n", "ϕ1 = 1.5 ;\n", "ϕ2 = -0.9 ;\n", "ϕ3 = 0.1 ;\n", "\n", "A = [ϕ1 ϕ2 ϕ3 ; 1 0 0 ; 0 1 0] ;\n", "Q = [θ0 ; 0; 0] ;\n", "H = [1 ; 0; 0] ;" ], "metadata": {}, "execution_count": 9 }, { "cell_type": "markdown", "source": [ "Solve:" ], "metadata": {} }, { "outputs": [], "cell_type": "code", "source": [ "ex2a = Drip(κ,β,A,Q,H;tol=1e-8);" ], "metadata": {}, "execution_count": 10 }, { "cell_type": "markdown", "source": [ "Measure performance for different values of $\\kappa$:" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "BenchmarkTools.Trial: \n memory estimate: 147.59 KiB\n allocs estimate: 1044\n --------------\n minimum time: 155.353 μs (0.00% GC)\n median time: 622.000 μs (0.00% GC)\n mean time: 680.100 μs (11.06% GC)\n maximum time: 28.590 ms (96.80% GC)\n --------------\n samples: 7332\n evals/sample: 1" }, "metadata": {}, "execution_count": 11 } ], "cell_type": "code", "source": [ "@benchmark Drip(κ,β,A,Q,H;tol=1e-8) setup = (κ = 10*rand())" ], "metadata": {}, "execution_count": 11 }, { "cell_type": "markdown", "source": [ "To be consistent with MMW(2018), scale the signal vector so that the weight on the first element is one ($h_1 = 1$):" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "0.05349220551890679" }, "metadata": {}, "execution_count": 12 } ], "cell_type": "code", "source": [ "h1 = 1;\n", "h2 = ex2a.ss.Y[2]/ex2a.ss.Y[1]\n", "h3 = ex2a.ss.Y[3]/ex2a.ss.Y[1]" ], "metadata": {}, "execution_count": 12 }, { "cell_type": "markdown", "source": [ "Print the weights:" ], "metadata": {} }, { "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " h1 = 1.000, h2 = -0.475, h3 = 0.053\n" ] } ], "cell_type": "code", "source": [ "s = @sprintf(\" h1 = %5.3f, h2 = %5.3f, h3 = %5.3f\", h1, h2, h3) ;\n", "println(s) ;" ], "metadata": {}, "execution_count": 13 }, { "cell_type": "markdown", "source": [ "Since we scaled the signal vector, we also need to adjust the noise in the signal accordingly:" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "0.7839654755293674" }, "metadata": {}, "execution_count": 14 } ], "cell_type": "code", "source": [ "AdjNoise = 3.879\n", "AdjPara_ex2a= AdjNoise*ex2a.ss.Y[1]" ], "metadata": {}, "execution_count": 14 }, { "cell_type": "markdown", "source": [ "Calculate IRFs:" ], "metadata": {} }, { "outputs": [], "cell_type": "code", "source": [ "irf_ex2a = irfs(ex2a; T=30) ;\n", "xirf_ex2a = irf_ex2a.x[1,1,:]\n", "xhatirf_ex2a= irf_ex2a.x_hat[1,1,:]\n", "\n", "x = zeros(3,30);\n", "xhat_noise_ex2a = zeros(3,30);\n", "\n", "for ii in 1:30\n", " if ii==1\n", " xhat_noise_ex2a[:,ii] = ex2a.ss.K;\n", " else\n", " xhat_noise_ex2a[:,ii] = A*xhat_noise_ex2a[:,ii-1]+(ex2a.ss.K*ex2a.ss.Y')*(x[:,ii]-A*xhat_noise_ex2a[:,ii-1]);\n", " end\n", "end\n", "xhat_noise_ex2a = xhat_noise_ex2a[1,:]*AdjPara_ex2a ;" ], "metadata": {}, "execution_count": 15 }, { "cell_type": "markdown", "source": [ "#### The ARMA(2,1) Example:" ], "metadata": {} }, { "cell_type": "markdown", "source": [ "Initialize:" ], "metadata": {} }, { "outputs": [], "cell_type": "code", "source": [ "β = 1.0 ;\n", "θ0 = 0.5 ;\n", "θ1 = -0.1 ;\n", "κ = 1.79 ;\n", "\n", "ϕ1 = 1.3 ;\n", "ϕ2 = -0.4 ;\n", "\n", "A = [ϕ1 ϕ2 θ1 ; 1.0 0.0 0.0 ; 0.0 0.0 0.0] ;\n", "Q = [θ0 ; 0.0; 1.0] ;\n", "H = [1.0; 0.0; 0.0] ;" ], "metadata": {}, "execution_count": 16 }, { "cell_type": "markdown", "source": [ "Solve:" ], "metadata": {} }, { "outputs": [], "cell_type": "code", "source": [ "ex2b = Drip(κ,β,A,Q,H,tol=1e-8);" ], "metadata": {}, "execution_count": 17 }, { "cell_type": "markdown", "source": [ "Measure performance for different values of $\\kappa$:" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "BenchmarkTools.Trial: \n memory estimate: 109.88 KiB\n allocs estimate: 777\n --------------\n minimum time: 150.033 μs (0.00% GC)\n median time: 441.626 μs (0.00% GC)\n mean time: 479.742 μs (9.89% GC)\n maximum time: 28.593 ms (98.00% GC)\n --------------\n samples: 10000\n evals/sample: 1" }, "metadata": {}, "execution_count": 18 } ], "cell_type": "code", "source": [ "@benchmark Drip(κ,β,A,Q,H;tol=1e-8) setup = (κ = 2*rand())" ], "metadata": {}, "execution_count": 18 }, { "cell_type": "markdown", "source": [ "To be consistent with MMW(2018), scale the signal vector so that the weight on the first element is one ($h_1 = 1$):" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "-0.0687478067895003" }, "metadata": {}, "execution_count": 19 } ], "cell_type": "code", "source": [ "h1 = 1;\n", "h2 = ex2b.ss.Y[2]/ex2b.ss.Y[1]\n", "h3 = ex2b.ss.Y[3]/ex2b.ss.Y[1]" ], "metadata": {}, "execution_count": 19 }, { "cell_type": "markdown", "source": [ "Print the weights:" ], "metadata": {} }, { "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " h1 = 1.000, h2 = -0.275, h3 = -0.069\n" ] } ], "cell_type": "code", "source": [ "s = @sprintf(\" h1 = %5.3f, h2 = %5.3f, h3 = %5.3f\", h1, h2, h3) ;\n", "println(s) ;" ], "metadata": {}, "execution_count": 20 }, { "cell_type": "markdown", "source": [ "Since we scaled the signal vector, we also need to adjust the noise in the signal accordingly:" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "0.3828466148956306" }, "metadata": {}, "execution_count": 21 } ], "cell_type": "code", "source": [ "AdjNoise = 1.349\n", "AdjPara_ex2b= AdjNoise*ex2b.ss.Y[1]" ], "metadata": {}, "execution_count": 21 }, { "cell_type": "markdown", "source": [ "Calculate the IRFs" ], "metadata": {} }, { "outputs": [], "cell_type": "code", "source": [ "irf_ex2b = irfs(ex2b; T=30) ;\n", "xirf_ex2b = irf_ex2b.x[1,1,:]\n", "xhatirf_ex2b = irf_ex2b.x_hat[1,1,:]\n", "\n", "x = zeros(3,30);\n", "xhat_noise_ex2b = zeros(3,30);\n", "\n", "for ii in 1:30\n", " if ii==1\n", " xhat_noise_ex2b[:,ii] = ex2b.ss.K;\n", " else\n", " xhat_noise_ex2b[:,ii] = A*xhat_noise_ex2b[:,ii-1]+(ex2b.ss.K*ex2b.ss.Y')*(x[:,ii]-A*xhat_noise_ex2b[:,ii-1]);\n", " end\n", "end\n", "xhat_noise_ex2b = xhat_noise_ex2b[1,:]*AdjPara_ex2b ;" ], "metadata": {}, "execution_count": 22 }, { "cell_type": "markdown", "source": [ "### Replication of Figure (5)" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "Plot{Plots.PyPlotBackend() n=6}", "image/png": 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\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n" ] }, "metadata": {}, "execution_count": 23 } ], "cell_type": "code", "source": [ "p1 = plot(1:30,[xirf_ex2a xhatirf_ex2a xhat_noise_ex2a],\n", " title = \"AR(3) example, optimal signal with i.i.d. noise\",\n", " color = [:black :gray20 :gray40], markerstrokecolor = [:black :gray20 :gray40],\n", " yticks = (-0.8:0.4:1.6),\n", " ylim = (-0.82,1.62))\n", "p2 = plot(1:30,[xirf_ex2b xhatirf_ex2b xhat_noise_ex2b],\n", " title = \"ARMA(2,1) example, optimal signal with i.i.d. noise\",\n", " color = [:black :gray20 :gray40], markerstrokecolor = [:black :gray20 :gray40],\n", " yticks = (0:0.1:0.6),\n", " ylim = (-0.05,0.65))\n", "Plots.plot(p1,p2,\n", " layout = (1,2),\n", " label = [L\"$X_{t}$ to $\\varepsilon_t$\" L\"$Y_t=E[X_t|I_t]$ to $\\varepsilon_t$\" L\"$Y_t=E[X_t|I_t]$ to $\\psi_t$\"],\n", " marker = [:circle :circle :star8], markercolor = [:black :false :gray40], markersize = [3 5 5],\n", " legend = :topright,\n", " xticks = (0:2:30),\n", " xlim = (0,30),\n", " lw = 1.5,\n", " legendfont = font(8), guidefont=font(9), titlefont=font(9), tickfont=font(8),\n", " size = (700,300),\n", " grid = :off, framestyle = :box)" ], "metadata": {}, "execution_count": 23 }, { "cell_type": "markdown", "source": [ "## Ex. 3: Price-setting with Rational Inattention" ], "metadata": {} }, { "cell_type": "markdown", "source": [ "We now replicate the price-setting exercise in MMW (2018) and its comparison with the Woodford (2002) example. This corresponds to Figure (6) in their paper.\n", "The model structure is identifcal to our [Example 1](https://github.com/afrouzi/DRIPs.jl/blob/master/examples/notebooks/ex1_pricing_pe_nofeedback.ipynb) (without strategic complementarity) and [Example 2](https://github.com/afrouzi/DRIPs.jl/blob/master/examples/notebooks/ex2_pricing_pe_with_feedback.ipynb) (with strategic complementarity)." ], "metadata": {} }, { "cell_type": "markdown", "source": [ "### The Case with No Strategic Complementarity" ], "metadata": {} }, { "cell_type": "markdown", "source": [ "Initialize:" ], "metadata": {} }, { "outputs": [], "cell_type": "code", "source": [ "ρ = 0.9; #persistence of money growth\n", "σ_u = 0.1; #std. deviation of shocks to money growth" ], "metadata": {}, "execution_count": 24 }, { "cell_type": "markdown", "source": [ "Primitives of the DRIP:" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "0.1" }, "metadata": {}, "execution_count": 25 } ], "cell_type": "code", "source": [ "κ = 0.62;\n", "β = 1.0;\n", "A = [1 ρ; 0 ρ];\n", "Q = σ_u*[1; 1];\n", "H = [1; 0];\n", "ω = 0.1" ], "metadata": {}, "execution_count": 25 }, { "cell_type": "markdown", "source": [ "Solve:" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "0.6196339094366835" }, "metadata": {}, "execution_count": 26 } ], "cell_type": "code", "source": [ "ex_opt = Drip(κ,β,A,Q,H,fcap=true);\n", "capa_opt = DRIPs.capacity(ex_opt) #returns capacity utilized in bits" ], "metadata": {}, "execution_count": 26 }, { "cell_type": "markdown", "source": [ "Measure performance for different values of $\\kappa$:" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "BenchmarkTools.Trial: \n memory estimate: 206.02 KiB\n allocs estimate: 1988\n --------------\n minimum time: 106.671 μs (0.00% GC)\n median time: 242.947 μs (0.00% GC)\n mean time: 442.516 μs (11.83% GC)\n maximum time: 38.122 ms (0.00% GC)\n --------------\n samples: 10000\n evals/sample: 1" }, "metadata": {}, "execution_count": 27 } ], "cell_type": "code", "source": [ "@benchmark Drip(κ,β,A,Q,H,fcap=true) setup = (κ = 2*rand())" ], "metadata": {}, "execution_count": 27 }, { "cell_type": "markdown", "source": [ "Calculate IRFs:" ], "metadata": {} }, { "outputs": [], "cell_type": "code", "source": [ "irfs_ex_opt = irfs(ex_opt, T = 12);\n", "output_opt = (irfs_ex_opt.x[1,1,:] - irfs_ex_opt.a[1,1,:]) ;\n", "output_opt = [0;output_opt] ;" ], "metadata": {}, "execution_count": 28 }, { "cell_type": "markdown", "source": [ "Now, to compare with Woodford (2002), assume that firms observe a noisy signal, $S_t = q_t + \\zeta_t$ where $\\zeta_t$ is an idiosyncratic noise. We first define a function to solve the corresponding Kalman filtering problem." ], "metadata": {} }, { "outputs": [], "cell_type": "code", "source": [ "function K_filtering(A,Q,Ysignal,Σz,Σ0 ; maxit=10000,tol=1e-10,w=1)\n", " err = 1\n", " iter = 0\n", " while (err > tol) & (iter < maxit)\n", " global Knew = Σ0*Ysignal*inv(Ysignal'*Σ0*Ysignal .+ Σz)\n", " global Σp_temp = Σ0 - Knew*Ysignal'*Σ0\n", " global Σ1 = A*Σp_temp*A' + Q*Q'\n", "\n", " err = norm(Σ1 - Σ0,2)/norm(Σ0,2)\n", " Σ0 = w*Σ1 + (1-w)*Σ0\n", "\n", " #println(\"Iteration $iter. Difference: $err\")\n", " iter += 1\n", " end\n", "\n", " return(Knew,Σ0,Σp_temp)\n", "end;" ], "metadata": {}, "execution_count": 29 }, { "cell_type": "markdown", "source": [ "Now find the capacity utilized under Woodford’s formulation such that it yields the same information flow as the optimal signal under ratinoal inattention." ], "metadata": {} }, { "outputs": [], "cell_type": "code", "source": [ "Ywoodford = [1;0]\n", "Σ1_init = ex_opt.ss.Σ_1\n", "\n", "Σz_new_b = 0.01\n", "Σz_new_u = 0.1\n", "Σz_new = (Σz_new_b+Σz_new_u)/2\n", "for i in 1:10000\n", " (Knew,Σ1_new,Σp_temp) = K_filtering(A,Q,Ywoodford,Σz_new,Σ1_init;w=0.5)\n", " capa_woodford = 0.5*log(det(Σ1_new)/det(Σp_temp))/log(2)\n", "\n", " if capa_woodford > capa_opt\n", " global Σz_new_b = Σz_new\n", " else\n", " global Σz_new_u = Σz_new\n", " end\n", " global Σz_new = (Σz_new_b+Σz_new_u)/2\n", " err = abs(capa_woodford - capa_opt)\n", " if err < 1e-5\n", " break\n", " end\n", "end" ], "metadata": {}, "execution_count": 30 }, { "cell_type": "markdown", "source": [ "Calculate impulse responses under Woodford's formulation" ], "metadata": {} }, { "outputs": [], "cell_type": "code", "source": [ "e_k = 1;\n", "x = zeros(2,12);\n", "xhat= zeros(2,12);\n", "a = zeros(2,12);\n", "\n", "for ii in 1:12\n", " if ii==1\n", " x[:,ii] = Q*e_k;\n", " xhat[:,ii] = (Knew*Ywoodford')*(x[:,ii]);\n", " else\n", " x[:,ii] = A*x[:,ii-1];\n", " xhat[:,ii] = A*xhat[:,ii-1]+(Knew*Ywoodford')*(x[:,ii]-A*xhat[:,ii-1]);\n", " end\n", " a[:,ii] .= H'*xhat[:,ii];\n", "end\n", "\n", "output_woodford = (x[1,:] - a[1,:]) ;\n", "output_woodford = [0;output_woodford] ;" ], "metadata": {}, "execution_count": 31 }, { "cell_type": "markdown", "source": [ "Before plotting the IRFs, we also solve the case with strategic complementarity." ], "metadata": {} }, { "cell_type": "markdown", "source": [ "### The Case with Strategic Complementarity" ], "metadata": {} }, { "cell_type": "markdown", "source": [ "We now turn to the example with strategic complementarity.\n", "As in our [Example 2](https://github.com/afrouzi/DRIPs.jl/blob/master/examples/notebooks/ex2_pricing_pe_with_feedback.ipynb), we first define a function to solve the fixed point with endogenous feedback." ], "metadata": {} }, { "outputs": [], "cell_type": "code", "source": [ "function ge_drip(ω,β,A,Q, #primitives of drip except for H because H is endogenous\n", " α, #strategic complementarity\n", " Hq, #state space rep. of Δq\n", " L; #length of truncation\n", " H0 = Hq, #optional: initial guess for H (Hq is the true solution when α=0)\n", " maxit = 200, #optional: max number of iterations for GE code\n", " tol = 1e-4) #optional: tolerance for iterations\n", " err = 1;\n", " iter = 0;\n", " M = [zeros(1,L-1) 0; Matrix(I,L-1,L-1) zeros(L-1,1)];\n", " while (err > tol) & (iter < maxit)\n", " if iter == 0\n", " global ge = Drip(ω,β,A,Q,H0; w=0.9);\n", " else\n", " global ge = Drip(ω,β,A,Q,H0; Ω0=ge.ss.Ω, Σ0=ge.ss.Σ_1, maxit=100);\n", " end\n", "\n", " XFUN(jj) = ((I-ge.ss.K*ge.ss.Y')*ge.A)^jj * (ge.ss.K*ge.ss.Y') * (M')^jj\n", " X = DRIPs.infinitesum(XFUN; maxit=L, start = 0); #E[x⃗]=X×x⃗\n", "\n", " XpFUN(jj) = α^jj * X^(jj)\n", " Xp = DRIPs.infinitesum(XpFUN; maxit=L, start = 0);\n", "\n", " H1 = (1-α)*Xp'*Hq;\n", " err= 0.5*norm(H1-H0,2)/norm(H0)+0.5*err;\n", " H0 = H1;\n", "\n", " iter += 1;\n", " if iter == maxit\n", " print(\"GE loop hit maxit\\n\")\n", " elseif mod(iter,10) == 0\n", " println(\"Iteration $iter. Difference: $err\")\n", " end\n", "\n", " end\n", " print(\" Iteration Done.\\n\")\n", " return(ge)\n", "end;" ], "metadata": {}, "execution_count": 32 }, { "cell_type": "markdown", "source": [ "Now, we solve for the optimal signal structure under rational inattention." ], "metadata": {} }, { "cell_type": "markdown", "source": [ "Initialize:" ], "metadata": {} }, { "outputs": [], "cell_type": "code", "source": [ "ρ = 0.9; #persistence of money growth\n", "σ_u = 0.1; #std. deviation of shocks to money growth\n", "α = 0.85; #degree of strategic complementarity\n", "L = 40; #length of truncation\n", "Hq = ρ.^(0:L-1); #state-space rep. of Δq\n", "\n", "ω = 0.08;\n", "β = 1 ;\n", "A = [1 zeros(1,L-2) 0; Matrix(I,L-1,L-1) zeros(L-1,1)];\n", "M = [zeros(1,L-1) 0; Matrix(I,L-1,L-1) zeros(L-1,1)]; # shift matrix\n", "Q = [σ_u; zeros(L-1,1)];" ], "metadata": {}, "execution_count": 33 }, { "cell_type": "markdown", "source": [ "Solve:" ], "metadata": {} }, { "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 10. Difference: 0.0019552334926346503\n", " Iteration Done.\n" ] } ], "cell_type": "code", "source": [ "ex_ge = ge_drip(ω,β,A,Q,α,Hq,L) ;" ], "metadata": {}, "execution_count": 34 }, { "cell_type": "markdown", "source": [ "Print capacity utilized in with strategic complementarity:" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "0.5830085573346633" }, "metadata": {}, "execution_count": 35 } ], "cell_type": "code", "source": [ "capa_ge = DRIPs.capacity(ge)" ], "metadata": {}, "execution_count": 35 }, { "cell_type": "markdown", "source": [ "Measure performance for random values of ω" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "BenchmarkTools.Trial: \n memory estimate: 349.28 MiB\n allocs estimate: 43572\n --------------\n minimum time: 273.563 ms (1.90% GC)\n median time: 281.564 ms (3.68% GC)\n mean time: 283.873 ms (3.45% GC)\n maximum time: 303.029 ms (3.42% GC)\n --------------\n samples: 18\n evals/sample: 1" }, "metadata": {}, "execution_count": 36 } ], "cell_type": "code", "source": [ "using Suppressor\n", "@suppress @benchmark ge_drip(ω,β,A,Q,α,Hq,L) setup = (ω = 0.1*rand())" ], "metadata": {}, "execution_count": 36 }, { "cell_type": "markdown", "source": [ "Calculate IRFs" ], "metadata": {} }, { "outputs": [], "cell_type": "code", "source": [ "geirfs = irfs(ex_ge,T = L) ;\n", "\n", "dq = diagm(Hq)*geirfs.x[1,1,:];\n", "q = inv(I-M)*dq ;\n", "output_ge_opt = q - geirfs.a[1,1,:] ;\n", "output_ge_opt = [0;output_ge_opt] ;" ], "metadata": {}, "execution_count": 37 }, { "cell_type": "markdown", "source": [ "Finally, to compare with the IRFs under Woodford (2002)’s specification, we find signal noise such that it yields the same information flow as the optimal signal structure." ], "metadata": {} }, { "outputs": [], "cell_type": "code", "source": [ "Ywoodford_ge = Hq\n", "Σ1_init = ex_ge.ss.Σ_1\n", "\n", "Σz_new_b = 0.05\n", "Σz_new_u = 0.12\n", "Σz_new = (Σz_new_b+Σz_new_u)/2\n", "\n", "for i in 1:10000\n", " (Knew,Σ1_new,Σp_temp) = K_filtering(A,Q,Ywoodford_ge,Σz_new,Σ1_init;w=0.5)\n", " capa_woodford = 0.5*log(det(Σ1_new)/det(Σp_temp))/log(2)\n", "\n", " if capa_woodford > capa_ge\n", " global Σz_new_b = Σz_new\n", " else\n", " global Σz_new_u = Σz_new\n", " end\n", " global Σz_new = (Σz_new_b+Σz_new_u)/2\n", " err = abs(capa_woodford - capa_ge)\n", " if err < 1e-5\n", " break\n", " end\n", "end\n", "\n", "XFUN(jj) = ((I-Knew*Ywoodford_ge')*A)^jj * (Knew*Ywoodford_ge') * (M')^jj\n", "X = DRIPs.infinitesum(XFUN; maxit=L, start = 0); #E[x⃗]=X×x⃗\n", "\n", "XpFUN(jj) = α^jj * X^(jj)\n", "Xp = DRIPs.infinitesum(XpFUN; maxit=L, start = 0);\n", "\n", "H1 = (1-α)*Xp'*Hq;" ], "metadata": {}, "execution_count": 38 }, { "cell_type": "markdown", "source": [ "Calculate impurse responses under Woodford's signal" ], "metadata": {} }, { "outputs": [], "cell_type": "code", "source": [ "e_k = 1;\n", "x = zeros(40,40);\n", "xhat= zeros(40,40);\n", "a = zeros(1,40);\n", "\n", "for ii in 1:40\n", " if ii==1\n", " x[:,ii] = Q*e_k;\n", " xhat[:,ii] = (Knew*Ywoodford_ge')*(x[:,ii]);\n", " else\n", " x[:,ii] = A*x[:,ii-1];\n", " xhat[:,ii] = A*xhat[:,ii-1]+(Knew*Ywoodford_ge')*(x[:,ii]-A*xhat[:,ii-1]);\n", " end\n", " a[:,ii] .= H1'*xhat[:,ii];\n", "end\n", "dq = diagm(Hq)*x[1,:];\n", "q = inv(I-M)*dq ;\n", "output_ge_woodford = q - a[1,:] ;\n", "output_ge_woodford = [0;output_ge_woodford] ;" ], "metadata": {}, "execution_count": 39 }, { "cell_type": "markdown", "source": [ "We now have all the IRFs to replicate Figure (6)" ], "metadata": {} }, { "cell_type": "markdown", "source": [ "### Replication of Figure (6)" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "Plot{Plots.PyPlotBackend() n=4}", "image/png": 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\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n" ] }, "metadata": {}, "execution_count": 40 } ], "cell_type": "code", "source": [ "p1 = plot(0:12,[output_woodford output_opt],\n", " title = L\"The case of $\\xi=1$\",\n", " color = [:black :gray40], markerstrokecolor = [:black :gray40],\n", " ylim = (-0.003,0.053),\n", " ytick = (0:0.01:0.05))\n", "p2 = plot(0:12,[output_ge_woodford[1:13] output_ge_opt[1:13]],\n", " title = L\"The case of $\\xi=0.15$\",\n", " color = [:black :gray40], markerstrokecolor = [:black :gray40],\n", " ylim = (-0.005,0.105),\n", " ytick = (0:0.02:0.1))\n", "Plots.plot(p1,p2,\n", " layout = (2,1),\n", " label = [\"Woodford Model\" \"Model with optimal signals\"],\n", " legend = :topright,\n", " marker = [:circle :star8], markersize = [5 5], markercolor = [:false :gray40],\n", " xlim = (0,12),\n", " xtick = (0:1:12),\n", " xlabel = \"Time\",\n", " lw = 1.5,\n", " legendfont = font(8), titlefont = font(10), guidefont = font(9), tickfont = font(8),\n", " size = (700,400),\n", " grid = :off, framestyle = :box)" ], "metadata": {}, "execution_count": 40 }, { "cell_type": "markdown", "source": [ "## Ex. 4: Business Cycle Model with News Shocks\n", "In this section, we replicate the business cycle model with news shocks in Section 7 in Mackowiak, Matejka and Wiederholt (2018).\n", "\n", "### Setup\n", "\n", "#### Full-Information\n", "The techonology shock follows AR(1) process:\n", "$$\n", "\\begin{aligned}\n", "z_{t} = \\rho z_{t-1} + \\sigma \\varepsilon_{t-k}\n", "\\end{aligned}\n", "$$\n", "and the total labor input is:\n", "$$\n", "\\begin{aligned}\n", "n_{t} = \\int_0^1 n_{i,t} di .\n", "\\end{aligned}\n", "$$\n", "\n", "Under perfect information, the households chooses the utility-maximizing labor supply, all firms choose the profit-maximizing labor input, and the labor market clearing condition is:\n", "$$\n", "\\begin{aligned}\n", "\\frac{1-\\gamma}{\\psi + \\gamma}w_{t} = \\frac{1}{\\alpha}(z_t - w_t).\n", "\\end{aligned}\n", "$$\n", "Then, the market clearing wages and the equilibrium labor input are:\n", "$$\n", "\\begin{aligned}\n", " w_{t} & = \\frac{\\frac{1}{\\alpha}}{\\frac{1-\\gamma}{\\psi+\\gamma} + \\frac{1}{\\alpha}} z_t \\equiv \\xi z_t \\\\\n", " n_t & = \\frac{1}{\\alpha}(1-\\xi) z_t.\n", "\\end{aligned}\n", "$$\n", "\n", "\n", "#### Rational Inattention\n", "\n", "Firms wants to keep track of their ideal price,\n", "$$\n", "\\begin{aligned}\n", " n_{t}^* = \\frac{1}{\\alpha} z_t - \\frac{1}{\\alpha} \\frac{\\psi + \\gamma}{1 - \\gamma} n_t\n", "\\end{aligned}\n", "$$\n", "where $n_{t} = \\int_0^1 n_{i,t} di$. Then, firm $i$'s choice depends on its information set at time $t$:\n", "$$\n", "\\begin{aligned}\n", " n_{i,t} = E_{i,t} [n_{t}^*].\n", "\\end{aligned}\n", "$$\n", "\n", "Note that now the state space representation for $n_{t}^*$ is determined in the equilibrium. However, we know that this is a Guassian process and by Wold's theorem we can decompose it to its $MA(\\infty)$ representation:\n", "$$\n", "\\begin{aligned}\n", " n_{t}^*=\\Phi(L)\\varepsilon_t\n", "\\end{aligned}\n", "$$\n", "where $\\Phi(.)$ is a lag polynomial and $\\varepsilon_t$ is the shock to technology. Here, we have basically guessed that the process for $p_{i,t}^*$ is determined uniquely by the history of monetary shocks which requires that rational inattention errors of firms are orthogonal (See [Afrouzi (2020)](http://www.afrouzi.com/strategic_inattetion.pdf)). Our objective is to find $\\Phi(.)$.\n", "\n", "Now, as in our [Example 2](https://github.com/afrouzi/DRIPs.jl/blob/master/examples/notebooks/ex2_pricing_pe_with_feedback.ipynb), we can represent the problem in a matrix notation.\n", "\n", "### Initialization" ], "metadata": {} }, { "outputs": [], "cell_type": "code", "source": [ "β = 1 ; # Time preference\n", "γ = 1/3 ; # Inverse of intertemporal elasticity of substitution\n", "ψ = 0 ; # Inverse of Frisch elasticity\n", "α = 3/4 ; # Labor share in production function\n", "θ = -1/α*(ψ+γ)/(1-γ) ;\n", "ξ = θ/(θ-1) ;\n", "\n", "ρ = 0.9 ; #persistence of technology shocks\n", "σ = 1 ; #std. deviation of technology shocks\n", "ω = 6.5 ; # Information cost\n", "\n", "L = 40 ; #length of truncation\n", "k = 8 ; #news horizon\n", "\n", "M = [zeros(1,L-1) 0; Matrix(I,L-1,L-1) zeros(L-1,1)]; # shift matrix\n", "Hz = ρ.^(0:L-1)\n", "Hz = (M^k)*Hz\n", "\n", "A = M ;\n", "Q = [σ; zeros(L-1,1)] ;" ], "metadata": {}, "execution_count": 41 }, { "cell_type": "markdown", "source": [ "Also, define a function that solves the GE problem and returns the solution in a `Drip` structure:" ], "metadata": {} }, { "outputs": [], "cell_type": "code", "source": [ "function ge_drip(ω,β,A,Q, #primitives of drip except for H because H is endogenous\n", " α, #strategic complementarity\n", " θ,\n", " Hz, #state space rep. of z\n", " L; #length of truncation\n", " w_out = 0.5, #optional: initial guess for H (Hq is the true solution when α=0)\n", " H0 = Hz, #optional: initial guess for H (Hq is the true solution when α=0)\n", " maxit = 200, #optional: max number of iterations for GE code\n", " tol = 1e-6) #optional: tolerance for iterations\n", " err = 1;\n", " iter = 0;\n", " M = [zeros(1,L-1) 0; Matrix(I,L-1,L-1) zeros(L-1,1)];\n", " eye = Matrix(I,L,L)\n", " while (err > tol) & (iter < maxit)\n", " if iter == 0\n", " global ge = Drip(ω,β,A,Q,H0;w=0.5, tol=1e-8);\n", " else\n", " global ge = Drip(ω,β,A,Q,H0;w=0.9, tol=1e-8, Ω0=ge.ss.Ω, Σ0=ge.ss.Σ_1, maxit=1000);\n", " end\n", "\n", " XFUN(jj) = ((eye-ge.ss.K*ge.ss.Y')*ge.A)^jj * (ge.ss.K*ge.ss.Y') * (M')^jj\n", " X = DRIPs.infinitesum(XFUN; maxit=L, start = 0); #E[x⃗]=X×x⃗\n", "\n", " H1 = (1/α)*Hz + θ*X'*H0 ;\n", "\n", " err= 0.5*norm(H1-H0,2)/norm(H0)+0.5*err;\n", "\n", " H0 = w_out*H1 + (1.0-w_out)*H0 ;\n", "\n", " iter += 1;\n", " if iter == maxit\n", " print(\"GE loop hit maxit\\n\")\n", " elseif mod(iter,10) == 0\n", " println(\"Iteration $iter. Difference: $err\")\n", " end\n", "\n", " end\n", " print(\" Iteration Done.\\n\")\n", " return(ge)\n", "end;" ], "metadata": {}, "execution_count": 42 }, { "cell_type": "markdown", "source": [ "### Solve and Measure Performance" ], "metadata": {} }, { "cell_type": "markdown", "source": [ "Solve:" ], "metadata": {} }, { "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 10. Difference: 0.008423206111917462\n", "Iteration 20. Difference: 1.1340049707814608e-5\n", " Iteration Done.\n" ] } ], "cell_type": "code", "source": [ "ge = ge_drip(ω,β,A,Q,α,θ,Hz,L) ;" ], "metadata": {}, "execution_count": 43 }, { "cell_type": "markdown", "source": [ "Measure performance by solving the model for different values of ω:" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "BenchmarkTools.Trial: \n memory estimate: 573.41 MiB\n allocs estimate: 78643\n --------------\n minimum time: 546.028 ms (2.93% GC)\n median time: 642.400 ms (2.49% GC)\n mean time: 636.123 ms (2.83% GC)\n maximum time: 666.456 ms (2.39% GC)\n --------------\n samples: 8\n evals/sample: 1" }, "metadata": {}, "execution_count": 44 } ], "cell_type": "code", "source": [ "@suppress @benchmark ge_drip(ω,β,A,Q,α,θ,Hz,L) setup = (ω = 6.5*rand())" ], "metadata": {}, "execution_count": 44 }, { "cell_type": "markdown", "source": [ "Calculate IRFs and profit loss:" ], "metadata": {} }, { "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "==: Profit loss from rational inattention = 0.00010\n" ] } ], "cell_type": "code", "source": [ "geirfs = irfs(ge,T = L) ;\n", "profit_loss = sum((geirfs.x[1,1,:]/100 - geirfs.x_hat[1,1,:]/100).^2) ;\n", "\n", "s = @sprintf(\"==: Profit loss from rational inattention = %6.5f\", profit_loss) ;\n", "println(s) ;" ], "metadata": {}, "execution_count": 45 }, { "cell_type": "markdown", "source": [ "### Replication of Figure (7)" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "Plot{Plots.PyPlotBackend() n=2}", "image/png": 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\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n" ] }, "metadata": {}, "execution_count": 46 } ], "cell_type": "code", "source": [ "n_opt = geirfs.a[1,1,:] ; # Optimal labor input under rational inattention\n", "n_fullinfo = σ*1/α*(1-ξ)*Hz ; # Optimal labor input under full information\n", "\n", "\n", "plot(1:30,[n_fullinfo[1:30] n_opt[1:30]],\n", " title = \"The impulse response of labor input to a productivity shock.\",\n", " ylabel = \"Percent\",\n", " label = [\"Equilibrium under perfect information\" \"Equilibirum when firms are subject to rational inattention\"],\n", " legend = :topright,\n", " color = [:black :gray40], markerstrokecolor = [:black :gray40],\n", " marker = [:circle :star8], markercolor = [:false :gray40], markersize = [5 5],\n", " ylim = (-0.05,0.85),\n", " ytick = (0:0.1:0.8),\n", " xlim = (0,30),\n", " xtick = (0:2:30),\n", " lw = 1.5,\n", " legendfont = font(8), titlefont = font(10), tickfont = font(8), guidefont = font(9),\n", " size = (650,400),\n", " grid = :off, framestyle = :box)" ], "metadata": {}, "execution_count": 46 }, { "cell_type": "markdown", "source": [ "---\n", "\n", "*This notebook was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*" ], "metadata": {} } ], "nbformat_minor": 3, "metadata": { "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "1.4.1" }, "kernelspec": { "name": "julia-1.4", "display_name": "Julia 1.4.1", "language": "julia" } }, "nbformat": 4 }