{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Replication of the Quantitative Analysis in Afrouzi and Yang (2020)"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "This notebook replicates the qunatitative analysis in [Afrouzi and Yang (2020)](http://www.afrouzi.com/dynamic_inattention.pdf) using the methods and the [solver](https://github.com/afrouzi/DRIPs.jl) from [Afrouzi and Yang (2020)](http://www.afrouzi.com/dynamic_inattention.pdf)."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Contents\n",
    "* Setup\n",
    "* A Three-Equation RI Model\n",
    "* Matrix Representation\n",
    "* Initialization\n",
    "* Functions\n",
    "* Solution\n",
    "    * Post-Volcker Calibration\n",
    "    * Pre-Volcker Calibration\n",
    "* Simulation\n",
    "* Impulse Response Functions"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Setup"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Households"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Households are fully rational and maximize their life-time utilities:\n",
    "$$\n",
    "\\begin{aligned}\n",
    "\\max & \\ \\ \\mathbb{E}_{t}^{f}\\left[\\sum_{t=0}^{\\infty}\\beta^{t}\\left(\\frac{C_{t}^{1-\\sigma}}{1-\\sigma}-\\frac{\\int_{0}^{1}L_{i,t}^{1+\\psi}di}{1+\\psi}\\right)\\right]\\\\\n",
    "\\text{s.t.} & \\ \\ \\int P_{i,t}C_{i,t} di+B_{t}\\leq R_{t-1}B_{t-1}+\\int_{0}^{1}W_{i,t}L_{i,t}di+\\Pi_{t}, \\quad \\text{for all } t\n",
    "\\end{aligned}\n",
    "$$\n",
    "where\n",
    "$$\n",
    "\\begin{aligned}\n",
    "C_{t} = \\left(\\int C_{i,t} ^{\\frac{\\theta-1}{\\theta}}di\\right)^{\\frac{\\theta}{\\theta-1}}.\n",
    "\\end{aligned}\n",
    "$$\n",
    "Here $\\mathbb{E}_{t}^{f}\\left[\\cdot\\right]$ is the full information rational expectation operator at time $t$. Since the main purpose of this paper is to study the effects of nominal rigidity and rational inattention among firms, I assume that the household is fully informed about all prices and wages. $B_{t}$ is the demand for nominal bond and $R_{t-1}$ is the nominal interest rate. $L_{i,t}$ is firm-specific labor supply of the household, $W_{i,t}$ is the firm-specific nominal wage, and $\\Pi_{t}$ is the aggregate profit from the firms. $C_{t}$ is the aggregator over the consumption for goods produced by firms. $\\theta$ is the constant elasticity of substitution across different firms.\n",
    "\n",
    "\n",
    "### Firms\n",
    "There is a measure one of firms, indexed by $i$, that operate in monopolistically competitive markets. Firms take wages and demands for their goods as given, and choose their prices $P_{i,t}$ based on their information set, $S_{i}^{t}$, at that time.  After setting their prices, firms hire labor from a competitive labor market and produce the realized level of demand that their prices induce with a production function,\n",
    "$$\n",
    "\\begin{aligned}\n",
    "    Y_{i,t} = A_{t} L_{i,t},\n",
    "\\end{aligned}\n",
    "$$\n",
    "where $L_{i,t}$ is firm $i$'s demand for labor. I assume that shocks to $A_{t}$ are independently and identically distributed and the log of the productivity shock, $a_{i,t}\\equiv\\log(A_{t})$, follows a AR(1) process:\n",
    "$$\n",
    "\\begin{aligned}\n",
    "    a_{t} = \\rho_a a_{t-1} + \\varepsilon_{a,t}, \\ \\   \\varepsilon_{a,t} \\sim N(0,\\sigma_{a}^2).\n",
    "\\end{aligned}\n",
    "$$\n",
    "Then, firm $i$'s nominal profit from sales of all goods at prices $\\{P_{i,j,t}\\}_{j=1}^N$ is given by\n",
    "$$\n",
    "\\begin{aligned}\n",
    "\\Pi_{i,t} (P_{i,t},A_{t},W_{i,t},P_t,Y_t) =\\left(P_{i,t}-W_{i,t}A_{t}\\right) \\left(\\frac{P_{i,t}}{P_{t}}\\right)^{-\\theta}Y_{t},\n",
    "\\end{aligned}\n",
    "$$\n",
    "where $Y_t$ is the nominal aggregate demand.\n",
    "\n",
    "At each period, firms optimally decide their prices and signals subject to costs of processing information. Firms are rationally inattentive in a sense that they choose their optimal information set by taking into account the cost of obtaining and processing information. At the beginning of period $t$, firm $i$ wakes up with its initial information set, $S_{i}^{t-1}$. Then it chooses optimal signals, $s_{i,t}$, from a set of available signals, $\\mathcal{S}_{i,t}$, subject to the cost of information which is linear in Shannon's mutual information function. Denote $ \\omega $ as the marginal cost of information processing. Firm $i$ forms a new information set, $S_i^t = S_i^{t-1} \\cup s_{i,t}$, and sets its new prices, $P_{i,t}$, based on that.\n",
    "\n",
    "The firm $i$ chooses a set of signals to observe over time $(s_{i,t} \\in  \\mathcal{S}_{i,t})_{t=0}^\\infty$ and a pricing strategy that maps the set of its prices at $t-1$ and its information set at $t$ to its optimal price at any given period, $P_{i,t}:(S_i^t)\\rightarrow \\mathbb{R}$ where $S_i^t=S_i^{t-1}\\cup s_{i,t} = S_i^{-1} \\cup \\{s_{i,\\tau}\\}_{\\tau=0}^t$ is the firm's information set at time $t$.  Then, the firm $i$'s problem is to maximize the net present value of its life time profits given an initial information set:\n",
    "$$\n",
    "\\begin{aligned}\n",
    "\\max_{\\{s_{i,t}\\in \\mathcal{S}_{i,t},P_{i,t} (S_i^t) \\}_{t\\geq0}} &  \\mathbb{E}\\left[\\sum_{t=0}^{\\infty}\\beta^t \\Lambda_t \\left\\{ \\Pi_{i,t} (P_{i,t},A_{t},W_{i,t},P_t,Y_t)  - \\omega \\mathbb{I} ( S_{i}^t ;  (A_{\\tau},W_{i,\\tau},P_\\tau,Y_\\tau)_{\\tau \\leq t} | S_i^{t-1} ) \\right\\} \\Bigg| S_{i}^{-1}\\right]  \\\\\n",
    "\\text{s.t.} \\quad & {S}_{i}^{t}={S}_{i}^{t-1}\\cup s_{i,t}\n",
    "\\end{aligned}\n",
    "$$\n",
    "where $\\Lambda_t$ is the stochastic discount factor and $\\mathbb{I} ( S_{i}^t ;  (A_{\\tau},W_{i,\\tau},P_\\tau,Y_\\tau)_{\\tau \\leq t} | S_i^{t-1} )$ is the Shannon's mutual information function.\n",
    "\n",
    "\n",
    "### Monetary Policy\n",
    "Monetary policy is specified as a standard Talor rule:\n",
    "$$\n",
    "\\begin{aligned}\n",
    "    R_t = (R_{t-1})^{\\rho} \\left( \\Pi_t^{\\phi_\\pi} \\left( \\frac{Y_t}{Y_t^n} \\right)^{\\phi_x} \\left(\\frac{Y_t}{Y_{t-1}} \\right)^{\\phi_{\\Delta y}} \\right)^{1-\\rho} \\exp(u_t)\n",
    "\\end{aligned}\n",
    "$$\n",
    "where $u_t \\sim N(0,\\sigma_{u}^2)$ is the monetary policy shock.\n",
    "\n",
    "## A Three-Equation GE Rational Inattention Model\n",
    "\n",
    "Our general equlibrium model is characterized by the following three equations with two stochastic processes of technology ($a_{t}$) and monetary policy shocks ($u_t$):\n",
    "$$\n",
    "\\begin{aligned}\n",
    "x_{t} & =\\mathbb{E}_{t}^{f}\\left[x_{t+1}-\\frac{1}{\\sigma}\\left(i_{t}-\\pi_{t+1}\\right)\\right]+\\mathbb{E}_{t}^{f}\\left[y_{t+1}^{n}\\right]-y_{t}^{n} \\\\\n",
    "p_{i,t} & =\\mathbb{E}_{i,t}\\left[p_{t} + \\alpha x_{t}\\right] \\\\\n",
    "i_{t} & =\\rho i_{t-1} + \\left( 1 - \\rho \\right) \\left(\\phi_{\\pi} \\pi_{t}+\\phi_{x} x_{t} - \\phi_{\\Delta y} \\Delta y_t \\right) + u_{t}\n",
    "\\end{aligned}\n",
    "$$\n",
    "where $\\mathbb{E}_{i,t}[\\cdot]$ is the firm $i$'s expectation operator conditional on her time $t$ information set, $x_t = y_t - y_t^n$ is the output gap, $y_{t}^{n}=\\frac{1+\\psi}{\\sigma+\\psi}a_{t}$ is the natural level of output, $i_t$ is the nominal interest rate, and $\\alpha = \\frac{\\sigma+\\psi}{1+\\psi\\theta}$ is the degree of strategic complementarity.\n",
    "\n",
    "\n",
    "## Matrix Representation\n",
    "\n",
    "Firms wants to keep track of their ideal price, $p_{i,t}^* = p_t + \\alpha x_t$. Notice that the state space representation for $p_{i,t}^*$ is no longer exogenous and 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",
    "$$\n",
    "\\begin{aligned}\n",
    "    p_{i,t}^*=\\Phi_a(L)\\varepsilon_{a,t} + \\Phi_u(L)\\varepsilon_{u,t}\n",
    "\\end{aligned}\n",
    "$$\n",
    "where $\\Phi_a(.)$ and $\\Phi_u(.)$ are lag polynomials. 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)](www.afrouzi.com/strategic_inattetion.pdf)).\n",
    "\n",
    "Since we cannot put $MA(\\infty)$ processes in the computer and have to truncate them. However, we know that for stationary processes we can arbitrarily get close to the true process by truncating $MA(\\infty)$ processes. Our problem here is that $p_{i,t}^*$ has a unit root and is not stationary. We can bypass this issue by re-writing the state space in the following way:\n",
    "$$\n",
    "\\begin{aligned}\n",
    "    p_{i,t}^*=\\Phi_a(L)\\varepsilon_{a,t} + \\phi_u(L)\\tilde{\\varepsilon}_{u,t},\\quad \\tilde{\\varepsilon}_{u,t}=(1-L)^{-1}\\varepsilon_{u,t} =\\sum_{j=0}^\\infty \\varepsilon_{u,t-j}\n",
    "\\end{aligned}\n",
    "$$\n",
    "here $\\tilde{\\varepsilon}_{u,t}$ is the unit root of the process and basically we have differenced out the unit root from the lag polynomial, and $\\phi_u(L)=(1-L)\\Phi_u(L)$. Notice that since the original process was difference stationary, differencing out the unit root means that $\\phi_u(L)$ is now in $\\ell_2$, and the process can now be approximated arbitrarily precisely with truncation.\n",
    "\n",
    "For ease of notation, let $z_t = (\\varepsilon_{a,t}, \\varepsilon_{u,t})$ and $\\tilde{z}_t = (\\varepsilon_{a,t}, \\tilde{\\varepsilon}_{u,t})$. For a length of truncation $L$, let $\\vec{x}_t' \\equiv ({z}_t,{z}_{t-1},\\dots,{z}_{t-(L+1)})\\in\\mathbb{R}^{2L}$ and $\\vec{\\mathbf{x}}_t' \\equiv (\\tilde{z}_t,\\tilde{z}_{t-1},\\dots,\\tilde{z}_{t-(L+1)})\\in\\mathbb{R}^{2L}$. Notice that\n",
    "$$\n",
    "\\begin{aligned}\n",
    "    \\vec{x}_t & = (\\mathbf{I} - \\mathbf{\\Lambda} \\mathbf{M}')\\vec{\\mathbf{x}}_t \\\\\n",
    "    \\vec{\\mathbf{x}}_t & = (\\mathbf{I} - \\mathbf{\\Lambda} \\mathbf{M}')^{-1}\\vec{x}_t\n",
    "\\end{aligned}\n",
    "$$\n",
    "where $\\mathbf{I}$ is a $2 \\times 2$ identity matrix, $\\mathbf{\\Lambda}$ is a diagonal matrix where $\\mathbf{\\Lambda}_{(2i,2i)} = 1$ and $\\mathbf{\\Lambda}_{(2i-1,2i-1)}=0$ for all $i={1,2,\\cdots,L}$, and $\\mathbf{M}$ is a shift matrix:\n",
    "$$\n",
    "\\begin{aligned}\n",
    "   \\mathbf{M} = \\left[\\begin{array}{ccccc}\n",
    "        0 & 0 & \\cdots & 0 & 0 & 0 & 0\\\\\n",
    "        0 & 0 & \\cdots & 0 & 0 & 0 & 0\\\\\n",
    "        1 & 0 & \\cdots & 0 & 0 & 0 & 0\\\\\n",
    "        0 & 1 & \\cdots & 0 & 0 & 0 & 0\\\\\n",
    "        \\vdots & \\vdots & \\ddots & \\vdots & \\vdots & \\vdots & \\vdots\\\\\n",
    "        0 & 0 & \\cdots & 1 & 0 & 0 & 0\\\\\n",
    "        0 & 0 & \\cdots & 0 & 1 & 0 & 0\n",
    "        \\end{array}\\right]\n",
    "\\end{aligned}\n",
    "$$\n",
    "Then, note that $p_{i,t}^*\\approx \\mathbf{H} '\\vec{\\mathbf{x}}_{t}$ where $\\mathbf{H}\\in \\mathbb{R}^{2L}$ is the truncated matrix analog of the lag polynominal, and is endogenous to the problem. Our objective is to find the general equilibrium $\\mathbf{H}$ along with the optimal information structure that it implies.\n",
    "\n",
    "Moreover, note that\n",
    "$$\n",
    "\\begin{aligned}\n",
    "a_t & = \\mathbf{H}_a'\\vec{x}_t,\\quad \\mathbf{H}_a'=(1,0,\\rho_a,0,\\rho_a^2,0,\\dots,\\rho_a^{L-1},0) \\\\\n",
    "u_t & = \\mathbf{H}_u'\\vec{x}_t,\\quad \\mathbf{H}_u'=(0,1,0,0,0,0,\\dots,0,0)\n",
    "\\end{aligned}\n",
    "$$\n",
    "We will solve for $\\mathbf{H}$ by iterating over the problem. In particular, in iteration $n\\geq 1$, given the guess $\\mathbf{H}_{(n-1)}$, we have the following state space representation for the firm's problem\n",
    "$$\n",
    "\\begin{aligned}\n",
    "   \\vec{\\mathbf{x}}_{t}&\n",
    "    = \\underset{\\mathbf{A}}{\\underbrace{\\left[\\begin{array}{ccccc}\n",
    "        0 & 0 & 0 & \\cdots & 0 & 0 & 0 & 0\\\\\n",
    "        0 & 1 & 0 & \\cdots & 0 & 0 & 0 & 0\\\\\n",
    "        1 & 0 & 0 & \\cdots & 0 & 0 & 0 & 0\\\\\n",
    "        0 & 1 & 0 & \\cdots & 0 & 0 & 0 & 0\\\\\n",
    "        0 & 0 & 1 & \\cdots & 0 & 0 & 0 & 0\\\\\n",
    "        \\vdots & \\vdots & \\vdots & \\ddots & \\vdots & \\vdots & \\vdots & \\vdots\\\\\n",
    "        0 & 0 & 0 & \\cdots & 1 & 0 & 0 & 0\\\\\n",
    "        0 & 0 & 0 & \\cdots & 0 & 1 & 0 & 0\n",
    "        \\end{array}\\right]}}\\, \\vec{\\mathbf{x}}_{t-1}\n",
    "    + \\underset{\\mathbf{Q}}{\\underbrace{\\left[\\begin{array}{cc}\n",
    "        1 & 0\\\\\n",
    "        0 & 1\\\\\n",
    "        0 & 0\\\\\n",
    "        \\vdots & \\vdots \\\\\n",
    "        0 & 0\n",
    "        \\end{array}\\right]}}\\, z_t, \\\\\n",
    "        p_{i,t}^*&=\\mathbf{H}_{(n-1)}'\\vec{\\mathbf{x}}_{t}\n",
    "\\end{aligned}\n",
    "$$\n",
    "Now, note that\n",
    "$$\n",
    "\\begin{aligned}\n",
    "    p_{t} &= \\int_{0}^{1} p_{i,t}di = \\mathbf{H}_{(n-1)}' \\int_{0}^{1}\\mathbb{E}_{i,t}[\\vec{\\mathbf{x}}_{t}]di \\\\\n",
    "                  &= \\mathbf{H}_{(n-1)}' \\sum_{j=0}^{\\infty}[(\\mathbf{I}-\\mathbf{K}_{(n)}\\mathbf{Y}'_{(n)})\\mathbf{A}]^{j}\\mathbf{K}_{(n)}\\mathbf{Y}'_{(n)}\\vec{\\mathbf{x}}_{t-j} \\\\\n",
    "                  &\\approx\\underset{\\equiv\\mathbf{X}_{(n)}}{\\mathbf{H}_{(n-1)}' \\underbrace{\\left[\\sum_{j=0}^{\\infty}[(\\mathbf{I}-\\mathbf{K}_{(n)}\\mathbf{Y}'_{(n)})\\mathbf{A}]^{j}\\mathbf{K}_{(n)}\\mathbf{Y}'_{(n)}\\mathbf{M}'^{j}\\right]}}\\vec{\\mathbf{x}}_{t} \\\\\n",
    "                  &= \\mathbf{H}_{(n-1)}' \\mathbf{X}_{(n)}\\vec{\\mathbf{x}}_{t} = \\mathbf{H}_{p}' \\vec{\\mathbf{x}}_{t}\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "\n",
    "\n",
    "Let $x_t = \\mathbf{H}_{x}'\\vec{x}_{t} $, $i_t = \\mathbf{H}_{i}'\\vec{x}_{t} $, and $\\pi_t = \\mathbf{H}_{\\pi}'\\vec{x}_{t} = \\mathbf{H}_{p}'(\\mathbf{I} - \\mathbf{\\Lambda} \\mathbf{M}')^{-1} (\\mathbf{I} - \\mathbf{M}') \\vec{x}_t $. Then from the households Euler equation, we have:\n",
    "$$\n",
    "\\begin{aligned}\n",
    "    & x_{t} =\\mathbb{E}_{t}^{f}\\left[x_{t+1}-\\frac{1}{\\sigma}\\left(i_{t}-\\pi_{t+1}\\right)\\right]+\\mathbb{E}_{t}^{f}[y_{t+1}^n] - y_t^n \\\\\n",
    "    \\Longrightarrow \\ \\ & \\mathbf{H}_{i} =\\sigma\\left(\\mathbf{M}'-\\mathbf{I}\\right)\\mathbf{H}_{x} + \\frac{\\sigma(1+\\psi)}{\\sigma+\\psi}\\left(\\mathbf{M}'-\\mathbf{I}\\right) \\mathbf{H}_{a}+\\mathbf{M}'\\mathbf{H}_{\\pi}\n",
    "\\end{aligned}\n",
    "$$\n",
    "Also, the Talyor rule gives:\n",
    "$$\n",
    "\\begin{aligned}\n",
    "& i_{t} =\\rho_{1}i_{t-1}+\\rho_{2}i_{t-2}+\\left(1-\\rho_{1}-\\rho_{2}\\right)\\left(\\phi_{\\pi}\\pi_{t}+\\phi_{x}x_{t}+\\phi_{\\Delta y}\\left(y_{t}-y_{t-1}\\right)\\right)+u_{t} \\\\\n",
    "\\Longrightarrow \\ \\ & \\left(\\mathbf{I}-\\rho_{1}\\mathbf{M}-\\rho_{2}\\mathbf{M}^{2}\\right)\\mathbf{H}_{i} =\\left(1-\\rho_{1}-\\rho_{2}\\right)\\phi_{\\pi}\\mathbf{H}_{\\pi}+\\left(1-\\rho_{1}-\\rho_{2}\\right)\\phi_{x}\\mathbf{H}_{x}\\\\\n",
    " & +\\left(1-\\rho_{1}-\\rho_{2}\\right)\\phi_{\\Delta y}\\left(\\mathbf{I}-\\mathbf{M}\\right)\\left(\\mathbf{H}_{x} + \\frac{1+\\psi}{\\sigma+\\psi} \\mathbf{H}_{a} \\right) + \\mathbf{H}_{u}\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "These give us $\\mathbf{H}_{x}$ and $\\mathbf{H}_{i}$ and we update new $\\mathbf{H}_{(n)}$ using:\n",
    "$$\n",
    "\\begin{aligned}\n",
    "    & \\mathbf{H}_{(n)} = \\mathbf{H}_{p} + \\alpha (\\mathbf{I} - \\mathbf{M} \\mathbf{\\Lambda}') \\mathbf{H}_{x}\n",
    "\\end{aligned}\n",
    "$$\n",
    "We iterate until convergence of $\\mathbf{H}_{(n)}$.\n",
    "\n",
    "## Initialization\n",
    "Load DRIPs solver and other packages:"
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "using DRIPs;\n",
    "using BenchmarkTools, LinearAlgebra, GLM, Statistics, Suppressor, Printf;\n",
    "using PyPlot; rc(\"text\", usetex=\"True\") ;\n",
    "              rc(\"font\",family=\"serif\",serif=:\"Palatino\") ;\n",
    "using Plots, LaTeXStrings; pyplot() ;"
   ],
   "metadata": {},
   "execution_count": 1
  },
  {
   "cell_type": "markdown",
   "source": [
    "Set parameters:"
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "struct param\n",
    "    β; σ; ψ; θ; α; # Deep parameters\n",
    "    ϕ_π; ϕ_x; ϕ_dy; ρ; # Monetary policy parameters\n",
    "    ρa; ρu; σa; σu; # Shock paramters\n",
    "end"
   ],
   "metadata": {},
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "source": [
    "###Assign model parameters"
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "σ       = 2.5       ; #Risk aversion\n",
    "β       = 0.99      ; #Time discount\n",
    "ψ       = 2.5       ; #Inverse of Frisch elasticity of labor supply\n",
    "θ       = 10        ; #Elasticity of substitution across firms\n",
    "α       = (σ+ψ)/(1+ψ*θ) ; #Strategic complementarity (1-α)"
   ],
   "metadata": {},
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "source": [
    "###Assign monetary policy parameters: post-Volcker"
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "ϕ_π     = 2.028     ; #Taylor rule response to inflation\n",
    "ϕ_x     = 0.673/4   ; #Taylor rule response to output\n",
    "ϕ_dy    = 3.122     ; #Taylor rule response to growth\n",
    "ρ       = 0.9457    ; #interest rate smoothing"
   ],
   "metadata": {},
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "source": [
    "###Assign monetary policy parameters: pre-Volcker"
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "ϕ_π_pre = 1.589     ; #Taylor rule response to inflation\n",
    "ϕ_x_pre = 1.167/4   ; #Taylor rule response to output\n",
    "ϕ_dy_pre= 1.028     ; #Taylor rule response to output growth\n",
    "ρ_pre   = 0.9181    ; #interest rate smoothing"
   ],
   "metadata": {},
   "execution_count": 5
  },
  {
   "cell_type": "markdown",
   "source": [
    "###Assign parameters governing shock processes"
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "ρu      = 0.0       ; #persistence of MP shock (post-Volcker)\n",
    "σu      = 0.279     ; #S.D. of MP shock (post-Volcker)\n",
    "ρu_pre  = 0.0       ; #persistence of MP shock (pre-Volcker)\n",
    "σu_pre  = 0.535     ; #S.D. of MP shock (pre-Volcker)"
   ],
   "metadata": {},
   "execution_count": 6
  },
  {
   "cell_type": "markdown",
   "source": [
    "###Calibrated parameters"
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "ρa      = 0.85      ; #persistence of technology shock\n",
    "σa      = 1.56      ; #S.D. of technology shock\n",
    "ω       = 0.773     ; #marginal cost of information"
   ],
   "metadata": {},
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "source": [
    "###Parameters for simulation/irfs"
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "simT    = 50000     ;\n",
    "nburn   = 500       ;\n",
    "T       = 20        ;"
   ],
   "metadata": {},
   "execution_count": 8
  },
  {
   "cell_type": "markdown",
   "source": [
    "Primitives of Drip"
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "numshock= 2         ; #number of shocks\n",
    "L       = 160       ; #length of trunction\n",
    "M       = [zeros(1,L-1) 0; Matrix(I,L-1,L-1) zeros(L-1,1)];\n",
    "M       = M^2       ;\n",
    "J       = zeros(L,L); J[2,2] = 1 ;\n",
    "Lambda  = zeros(L,L);\n",
    "for i = 1:Int64(L/numshock); Lambda[i*numshock,i*numshock] = 1; end\n",
    "A       = M + J     ;\n",
    "eye     = Matrix(I,L,L);"
   ],
   "metadata": {},
   "execution_count": 9
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Functions\n",
    "We start with a function that solves the GE problem and returns the solution in a `Drip` structure:"
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "function agg_drip(p::param,ω,A,Q,M,Lambda,\n",
    "                  Ha,               #state space rep. of a\n",
    "                  Hu;               #state space rep. of u\n",
    "                  H0    = Hu+Ha,    #optional: initial guess for H0\n",
    "                  Sigma = A*A'+Q*Q',#optional: initial guess for Σ_0\n",
    "                  Omega = H0*H0',   #optional: initial guess for Ω\n",
    "                  maxit = 10000,    #optional: max. iterations for GE code\n",
    "                  maxit_in = 100,   #optional: max. iterations for solving DRIP\n",
    "                  tol   = 1e-4,     #optional: tolerance for iterations\n",
    "                  w     = 1)        #optional: update weight for RI\n",
    "    err   = 1;\n",
    "    iter  = 1;\n",
    "    L     = length(H0);\n",
    "    eye   = Matrix(I,L,L);\n",
    "\n",
    "    temp0 = (eye-M)*inv(eye - M*Lambda') ;\n",
    "    Htemp1 = (p.σ*(eye-p.ρ*M)*(M'-eye) - (1-p.ρ)*(p.ϕ_x*eye + p.ϕ_dy*(eye-M))) ;\n",
    "    Htemp2 = (1-p.ρ)*p.ϕ_π*eye - (eye-p.ρ*M)*M' ;\n",
    "    Htemp3 = (1+p.ψ)/(p.σ+p.ψ)*((1-p.ρ)*p.ϕ_dy*(eye-M) - p.σ*(eye-p.ρ*M)*(M'-eye)) ;\n",
    "\n",
    "    while (err > tol) & (iter < maxit)\n",
    "\n",
    "        if iter == 1\n",
    "            global ge  = Drip(ω,p.β,A,Q,H0;\n",
    "                              Ω0=Omega, Σ0=Sigma, w=w, maxit=maxit_in);\n",
    "        else\n",
    "            global ge  = Drip(ω,p.β,A,Q,H0;\n",
    "                              Ω0=ge.ss.Ω, Σ0=ge.ss.Σ_1, w=w, maxit=maxit_in);\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=200, start = 0);  #E[x⃗]=X×x⃗\n",
    "\n",
    "        global Hp = X'*H0 ;\n",
    "        global Hπ = temp0*Hp ;\n",
    "\n",
    "        Hπ[L-20:end,:] .= 0\n",
    "\n",
    "        global Hx = (Htemp1)\\(Htemp2*Hπ + Htemp3*Ha + Hu) ;\n",
    "\n",
    "        global H1 = Hp + p.α*(eye - M*Lambda')*Hx ;\n",
    "\n",
    "        err= 0.5*norm(H1-H0,2)/norm(H0)+0.5*err;\n",
    "\n",
    "        H0 = H1;\n",
    "\n",
    "        cap = DRIPs.capacity(ge, unit = \"bit\")\n",
    "\n",
    "        if iter == maxit\n",
    "            print(\"***GE loop hit maxit -- no convergence\\n\")\n",
    "        elseif mod(iter,50) == 0\n",
    "            println(\"  Iteration $iter. Error: $err. Capacity: $cap.\")\n",
    "        end\n",
    "\n",
    "        iter += 1;\n",
    "    end\n",
    "\n",
    "    return(ge,H1,Hx,Hp,Hπ)\n",
    "end;"
   ],
   "metadata": {},
   "execution_count": 10
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Model Solution\n",
    "### Post-Volcker Calibration\n",
    "Start with post-Volcker calibration:"
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "Q       = zeros(L,2); Q[1,1]=σa; Q[2,2]=σu;\n",
    "\n",
    "Ha_post = ρa.^(0:1:L/2-1) ;\n",
    "Hu_post = ρu.^(0:1:L/2-1) ;\n",
    "Ha_post = kron(Ha_post,[1,0])[:,:] ;\n",
    "Hu_post = kron(Hu_post,[0,1])[:,:] ;\n",
    "\n",
    "p       = param(β,σ,ψ,θ,α,ϕ_π,ϕ_x,ϕ_dy,ρ,ρa,ρu,σa,σu) ;"
   ],
   "metadata": {},
   "execution_count": 11
  },
  {
   "cell_type": "markdown",
   "source": [
    "Get initial guess"
   ],
   "metadata": {}
  },
  {
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Get initial guess for post-Volcker solutions\n",
      " 55.478949 seconds (1.51 M allocations: 110.753 GiB, 3.43% gc time)\n"
     ]
    }
   ],
   "cell_type": "code",
   "source": [
    "print(\"\\nGet initial guess for post-Volcker solutions\\n\")\n",
    "@time begin\n",
    "    @suppress agg_drip(p,ω,A,Q,M,Lambda,Ha_post,Hu_post;\n",
    "                       w=0.95, maxit=300, maxit_in=5);\n",
    "end;"
   ],
   "metadata": {},
   "execution_count": 12
  },
  {
   "cell_type": "markdown",
   "source": [
    "Solve the model: Post-Volcker"
   ],
   "metadata": {}
  },
  {
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Solve for the post-Volcker model:\n",
      "  Iteration 50. Error: 0.00019955553628239823. Capacity: 1.1862271072482864.\n",
      "  Iteration 100. Error: 0.0001744872805571109. Capacity: 1.1857495656383183.\n",
      "  Iteration 150. Error: 0.00015864561622833798. Capacity: 1.18530140435572.\n",
      "  Iteration 200. Error: 0.00014625771320174962. Capacity: 1.1849474294457745.\n",
      "  Iteration 250. Error: 0.00013581998821178998. Capacity: 1.184782449923788.\n",
      "  Iteration 300. Error: 0.00012822204892082466. Capacity: 1.1844767889679662.\n",
      "  Iteration 350. Error: 0.00012052948044002251. Capacity: 1.184205165427262.\n",
      "  Iteration 400. Error: 0.0001177295005858932. Capacity: 1.1839682745514049.\n",
      "  Iteration 450. Error: 0.00011038503577128138. Capacity: 1.1838080473974388.\n",
      "  Iteration 500. Error: 0.00011286983266623046. Capacity: 1.1835142347515875.\n",
      "  Iteration 550. Error: 0.00011083164494455719. Capacity: 1.1835967086043597.\n",
      "115.656412 seconds (2.82 M allocations: 224.653 GiB, 2.66% gc time)\n"
     ]
    }
   ],
   "cell_type": "code",
   "source": [
    "print(\"\\nSolve for the post-Volcker model:\\n\");\n",
    "@time (ge_post,H1_post,Hx_post,Hp_post,Hπ_post) =\n",
    "            agg_drip(p,ω,A,Q,M,Lambda,Ha_post,Hu_post;\n",
    "                     H0    = H1,\n",
    "                     Sigma = ge.ss.Σ_1,\n",
    "                     Omega = ge.ss.Ω,\n",
    "                     w=0.95, maxit=5000, maxit_in=500) ;\n",
    "\n",
    "Hy_post = Hx_post + (1+ψ)/(σ+ψ)*Ha_post     ;\n",
    "Hi_post = M'*Hπ_post + σ*(M'-eye)*Hy_post   ;\n",
    "Hr_post = Hi_post - (M')*Hπ_post            ;"
   ],
   "metadata": {},
   "execution_count": 13
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Pre-Volcker Calibration\n",
    "Then, pre-Volcker calibration:"
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "Q_pre    = zeros(L,2); Q_pre[1,1]=σa; Q_pre[2,2]=σu_pre;\n",
    "\n",
    "Ha_pre   = ρa.^(0:1:L/2-1)          ;\n",
    "Hu_pre   = ρu_pre.^(0:1:L/2-1)      ;\n",
    "Ha_pre   = kron(Ha_pre,[1,0])[:,:]  ;\n",
    "Hu_pre   = kron(Hu_pre,[0,1])[:,:]  ;\n",
    "\n",
    "p_pre    = param(β,σ,ψ,θ,α,ϕ_π_pre,ϕ_x_pre,ϕ_dy_pre,\n",
    "                 ρ_pre,ρa,ρu_pre,σa,σu_pre) ;"
   ],
   "metadata": {},
   "execution_count": 14
  },
  {
   "cell_type": "markdown",
   "source": [
    "Get initial guess: Pre-Volcker"
   ],
   "metadata": {}
  },
  {
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Get initial guess for pre-Volcker solutions\n",
      " 54.951161 seconds (1.38 M allocations: 110.745 GiB, 2.55% gc time)\n"
     ]
    }
   ],
   "cell_type": "code",
   "source": [
    "print(\"\\nGet initial guess for pre-Volcker solutions\\n\")\n",
    "@time begin\n",
    "    @suppress agg_drip(p_pre,ω,A,Q_pre,M,Lambda,Ha_pre,Hu_pre;\n",
    "                       H0    = H1_post,\n",
    "                       Sigma = ge_post.ss.Σ_1,\n",
    "                       Omega = ge_post.ss.Ω,\n",
    "                       w=0.95, maxit=300, maxit_in=5) ;\n",
    "end;"
   ],
   "metadata": {},
   "execution_count": 15
  },
  {
   "cell_type": "markdown",
   "source": [
    "Solve the model: Pre-Volcker"
   ],
   "metadata": {}
  },
  {
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Solve for the pre-Volcker model:\n",
      "  Iteration 50. Error: 0.0001485454079821963. Capacity: 1.6642946059660233.\n",
      "  Iteration 100. Error: 0.00011573353266253216. Capacity: 1.664275383320826.\n",
      " 26.957388 seconds (651.97 k allocations: 52.173 GiB, 2.41% gc time)\n"
     ]
    }
   ],
   "cell_type": "code",
   "source": [
    "print(\"\\nSolve for the pre-Volcker model:\\n\");\n",
    "@time begin (ge_pre,H1_pre,Hx_pre,Hp_pre,Hπ_pre) =\n",
    "            agg_drip(p_pre,ω,A,Q_pre,M,Lambda,Ha_pre,Hu_pre;\n",
    "                     H0    = H1,\n",
    "                     Sigma = ge.ss.Σ_1,\n",
    "                     Omega = ge.ss.Ω,\n",
    "                     w=0.95, maxit=5000, maxit_in=500) ;\n",
    "end\n",
    "\n",
    "Hy_pre    = Hx_pre + (1+ψ)/(σ+ψ)*Ha_pre     ;\n",
    "Hi_pre    = M'*Hπ_pre + σ*(M'-eye)*Hy_pre   ;\n",
    "Hr_pre    = Hi_pre - (M')*Hπ_pre            ;"
   ],
   "metadata": {},
   "execution_count": 16
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Model Simulation\n",
    "Start with the simulation for post-Volcker:"
   ],
   "metadata": {}
  },
  {
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Simulate the models:\n",
      "==> Post-Volcker: std(π)=0.015, std(y)=0.018, corr(π,y)=0.209\n",
      "  3.061112 seconds (1.06 M allocations: 10.858 GiB, 12.18% gc time)\n"
     ]
    }
   ],
   "cell_type": "code",
   "source": [
    "print(\"\\nSimulate the models:\\n\");\n",
    "\n",
    "@time begin\n",
    "    sim_post    = simulate(ge_post; T=simT, burn=nburn, seed=1) ;\n",
    "\n",
    "    x_shock     = sim_post.x        ;\n",
    "    xhat_avg    = sim_post.x_hat    ;\n",
    "\n",
    "    sim_π       = (Hπ_post'*(eye-Lambda*M')*x_shock)' ;\n",
    "    sim_y       = (Hy_post'*(eye-Lambda*M')*x_shock)' ;\n",
    "    sim_x       = (Hx_post'*(eye-Lambda*M')*x_shock)' ;\n",
    "\n",
    "    mat_sim     = [sim_π sim_y sim_x]   ;\n",
    "    cor_sim     = cor(mat_sim)          ;\n",
    "\n",
    "    stat_post   = vec([std(sim_π/100) std(sim_y/100) cor_sim[2,1]]) ;\n",
    "\n",
    "    s = @sprintf(\"==> Post-Volcker: std(π)=%5.3f, std(y)=%5.3f, corr(π,y)=%5.3f\",\n",
    "                 stat_post[1], stat_post[2], stat_post[3])  ;\n",
    "    println(s) ;\n",
    "end"
   ],
   "metadata": {},
   "execution_count": 17
  },
  {
   "cell_type": "markdown",
   "source": [
    "Now simulate for pre-Volcker:"
   ],
   "metadata": {}
  },
  {
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==> Pre-Volcker : std(π)=0.025, std(y)=0.020, corr(π,y)=0.245\n",
      "  2.820915 seconds (1.06 M allocations: 10.858 GiB, 9.84% gc time)\n"
     ]
    }
   ],
   "cell_type": "code",
   "source": [
    "@time begin\n",
    "    sim_pre     = simulate(ge_pre; T=simT, burn=nburn, seed=1) ;\n",
    "\n",
    "    x_shock_pre = sim_pre.x     ;\n",
    "    xhat_avg_pre= sim_pre.x_hat ;\n",
    "\n",
    "    sim_π_pre   = (Hπ_pre'*(eye-Lambda*M')*x_shock_pre)';\n",
    "    sim_y_pre   = (Hy_pre'*(eye-Lambda*M')*x_shock_pre)';\n",
    "    sim_x_pre   = (Hx_pre'*(eye-Lambda*M')*x_shock_pre)';\n",
    "\n",
    "    mat_sim_pre = [sim_π_pre sim_y_pre sim_x_pre]   ;\n",
    "    cor_sim_pre = cor(mat_sim_pre)                  ;\n",
    "\n",
    "    stat_pre    = vec([std(sim_π_pre/100) std(sim_y_pre/100) cor_sim_pre[2,1]]);\n",
    "\n",
    "    s = @sprintf(\"==> Pre-Volcker : std(π)=%5.3f, std(y)=%5.3f, corr(π,y)=%5.3f\",\n",
    "                 stat_pre[1],stat_pre[2],stat_pre[3])  ;\n",
    "    println(s) ;\n",
    "end"
   ],
   "metadata": {},
   "execution_count": 18
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Impulse Response Functions\n",
    "Reshape variables for IRFs"
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "pi      = reshape(Hπ_post,numshock,Int64(L/numshock))' ;\n",
    "x       = reshape(Hx_post,numshock,Int64(L/numshock))' ;\n",
    "y       = reshape(Hy_post,numshock,Int64(L/numshock))' ;\n",
    "i       = reshape(Hi_post,numshock,Int64(L/numshock))' ;\n",
    "r       = reshape(Hr_post,numshock,Int64(L/numshock))' ;\n",
    "a       = reshape(Ha_post,numshock,Int64(L/numshock))' ;\n",
    "u       = reshape(Hu_post,numshock,Int64(L/numshock))' ;\n",
    "\n",
    "pi_pre  = reshape(Hπ_pre,numshock,Int64(L/numshock))' ;\n",
    "x_pre   = reshape(Hx_pre,numshock,Int64(L/numshock))' ;\n",
    "y_pre   = reshape(Hy_pre,numshock,Int64(L/numshock))' ;\n",
    "i_pre   = reshape(Hi_pre,numshock,Int64(L/numshock))' ;\n",
    "r_pre   = reshape(Hr_pre,numshock,Int64(L/numshock))' ;\n",
    "a_pre   = reshape(Ha_pre,numshock,Int64(L/numshock))' ;\n",
    "u_pre   = reshape(Hu_pre,numshock,Int64(L/numshock))' ;"
   ],
   "metadata": {},
   "execution_count": 19
  },
  {
   "cell_type": "markdown",
   "source": [
    "Plot IRFs:"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#Draw IRFs"
   ],
   "metadata": {}
  },
  {
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Plot{Plots.PyPlotBackend() n=16}",
      "image/png": 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",
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     "metadata": {},
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   "cell_type": "code",
   "source": [
    "title1 = Plots.plot(ylabel     = \"IRFs to Technoloygy Shock\",\n",
    "             guidefont  = font(10),\n",
    "             grid       = false, showaxis = false,\n",
    "             bottom_margin = 20Plots.px)\n",
    "title2 = Plots.plot(ylabel     = \"IRFs to Monetary Shock\",\n",
    "             guidefont  = font(10),\n",
    "             grid       = false, showaxis = false,\n",
    "             bottom_margin = -50Plots.px,\n",
    "             top_margin = 30Plots.px)\n",
    "\n",
    "p1 = Plots.plot(0:T,[σa*pi[1:T+1,1],σa*pi_pre[1:T+1,1]],\n",
    "        title           = L\"Inflation ($\\pi_t$)\",\n",
    "        ylabel          = \"Percent\",\n",
    "        guidefont       = font(9),\n",
    "        yticks          = -1:0.2:0.3,\n",
    "        label           = [\"Post-Volcker\" \"Pre-Volcker\"],\n",
    "        legend          = :bottomright,\n",
    "        legendfont      = font(8),\n",
    "        color           = [:black :darkgray],\n",
    "        linestyle       = [:solid :dashdot],\n",
    "        )\n",
    "p2 = Plots.plot(0:T,[σa*y[1:T+1,1],σa*y_pre[1:T+1,1]],\n",
    "        title           = L\"Output ($y_t$)\",\n",
    "        legend          = false,\n",
    "        color           = [:black :darkgray],\n",
    "        linestyle       = [:solid :dashdot],\n",
    "        )\n",
    "p3 = Plots.plot(0:T,[σa*i[1:T+1,1],σa*i_pre[1:T+1,1]],\n",
    "        title           = L\"Nominal Rate ($i_t$)\",\n",
    "        legend          = false,\n",
    "        yticks          = -0.15:0.03:0.0,\n",
    "        color           = [:black :darkgray],\n",
    "        linestyle       = [:solid :dashdot],\n",
    "        )\n",
    "p4 = Plots.plot(0:T,[σa*r[1:T+1,1],σa*r_pre[1:T+1,1]],\n",
    "        title           = L\"Real Rate ($r_t$)\",\n",
    "        legend          = false,\n",
    "        color           = [:black :darkgray],\n",
    "        linestyle       = [:solid :dashdot],\n",
    "        )\n",
    "p5 = Plots.plot(0:T,[-σu*pi[1:T+1,2],-σu_pre*pi_pre[1:T+1,2]],\n",
    "        title           = L\"Inflation ($\\pi_t$)\",\n",
    "        ylabel          = \"Percent\",\n",
    "        xlabel          = \"Time\",\n",
    "        guidefont       = font(9),\n",
    "        label           = [\"Post-Volcker\" \"Pre-Volcker\"],\n",
    "        legend          = :topright,\n",
    "        legendfont      = font(8),\n",
    "        color           = [:black :darkgray],\n",
    "        linestyle       = [:solid :dashdot],\n",
    "        )\n",
    "p6 = Plots.plot(0:T,[-σu*y[1:T+1,2],-σu_pre*y_pre[1:T+1,2]],\n",
    "        title           = L\"Output ($y_t$)\",\n",
    "        xlabel          = \"Time\",\n",
    "        guidefont       = font(9),\n",
    "        legend          = false,\n",
    "        color           = [:black :darkgray],\n",
    "        linestyle       = [:solid :dashdot],\n",
    "        )\n",
    "p7 = Plots.plot(0:T,[-σu*i[1:T+1,2],-σu_pre*i_pre[1:T+1,2]],\n",
    "        title           = L\"Nominal Rate ($i_t$)\",\n",
    "        xlabel          = \"Time\",\n",
    "        guidefont       = font(9),\n",
    "        legend          = false,\n",
    "        color           = [:black :darkgray],\n",
    "        linestyle       = [:solid :dashdot],\n",
    "        )\n",
    "p8 = Plots.plot(0:T,[-σu*r[1:T+1,2],-σu_pre*r_pre[1:T+1,2]],\n",
    "        title           = L\"Real Rate ($r_t$)\",\n",
    "        xlabel          = \"Time\",\n",
    "        guidefont       = font(9),\n",
    "        legend          = false,\n",
    "        color           = [:black :darkgray],\n",
    "        linestyle       = [:solid :dashdot],\n",
    "        )\n",
    "\n",
    "l = @layout [\n",
    "            a{0.001w} Plots.grid(1,4)\n",
    "            a{0.001w} Plots.grid(1,4)\n",
    "            ]\n",
    "\n",
    "Plots.plot(title1,p1,p2,p3,p4,title2,p5,p6,p7,p8,\n",
    "        layout      = l,\n",
    "        gridstyle   = :dot,\n",
    "        gridalpha   = 0.2,\n",
    "        lw          = [2.5 2],\n",
    "        titlefont   = font(10),\n",
    "        xticks      = (0:4:T),\n",
    "        xlim        = (0,T),\n",
    "        tickfont    = font(8),\n",
    "        size        = (900,550),\n",
    "        framestyle  = :box)"
   ],
   "metadata": {},
   "execution_count": 20
  },
  {
   "cell_type": "markdown",
   "source": [
    "---\n",
    "\n",
    "*This notebook was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*"
   ],
   "metadata": {}
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