{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Monetary Economics : Chapter 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preliminaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This line configures matplotlib to show figures embedded in the notebook, \n",
    "# instead of opening a new window for each figure. More about that later. \n",
    "# If you are using an old version of IPython, try using '%pylab inline' instead.\n",
    "%matplotlib inline\n",
    "\n",
    "from pysolve.model import Model\n",
    "from pysolve.utils import is_close,round_solution\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model INSOUTB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_insoutb_model():\n",
    "    model = Model()\n",
    "\n",
    "    model.set_var_default(0)\n",
    "    model.var('Ad', desc='Demand for Central bank advances from commercial banks')\n",
    "    model.var('As', desc='Supply of central bank advances to commercial banks')\n",
    "    model.var('Bbd', desc='Government bills demanded by commercial banks')\n",
    "    model.var('Bbdn', desc='Notional demand for government bills from commercial banks')\n",
    "    model.var('Bcb', desc='Government bills held by Central bank')\n",
    "    model.var('Bhd', desc='Demand for government bills')\n",
    "    model.var('Bhh', desc='Government bills held by households')\n",
    "    model.var('Bs', desc='Supply of government bills')\n",
    "    model.var('BLd', desc='Demand for government bonds')\n",
    "    model.var('BLh', desc='Demand for government bonds')\n",
    "    model.var('BLs', desc='Supply of government bonds')\n",
    "    model.var('BLR', desc='Gross bank liquidity ratio')\n",
    "    model.var('BLRn', desc='Net bank liquidity ratio')\n",
    "    model.var('BPM', desc=\"Banks' profit margin\")\n",
    "    model.var('Ck', desc='Real consumption')\n",
    "    model.var('CG', desc='Capital gains on government bonds')\n",
    "    model.var('CONS', desc='Consumption at current prices')\n",
    "    model.var('F', desc='Realized profits of firms and banks')\n",
    "    model.var('Fb', desc='Realized profits of firms and banks')\n",
    "    model.var('Fcb', desc='Central bank \"profits\"')\n",
    "    model.var('Ff', desc='Realized firm profits')\n",
    "    model.var('Ffe', desc='Expected profits of firms')\n",
    "    model.var('G', desc='Government expenditures')\n",
    "    model.var('Hbd', desc='Cash required by banks')\n",
    "    model.var('Hbs', desc='Cash supplied to banks')\n",
    "    model.var('Hhd', desc='Household demand for cash')\n",
    "    model.var('Hhh', desc='Cash held by households')\n",
    "    model.var('Hhs', desc='Cash supplied by households')\n",
    "    model.var('Hs', desc='Total supply of cash')\n",
    "    model.var('IN', desc='Stock of inventories at current costs')\n",
    "    model.var('INk', desc='Real inventories')\n",
    "    model.var('INke', desc='Expected real inventories')\n",
    "    model.var('INkt', desc='Target level of real inventories')\n",
    "    model.var('Ld', desc='Demand for loans')\n",
    "    model.var('Ls', desc='Supply of loans')\n",
    "    model.var('M1h', desc='Checking deposits held by households')\n",
    "    model.var('M1hn', desc='Notional holding of checking deposits')\n",
    "    model.var('M1s', desc='Checking deposits supplied by banks')\n",
    "    model.var('M2d', desc='Demand for term deposits - constrained to be non-negative')\n",
    "    model.var('M2h', desc='Term deposits held by households')\n",
    "    model.var('M2s', desc='Term deposits supplied by banks')\n",
    "    model.var('N', desc='Employment level')\n",
    "    model.var('NHUC', desc='Normal historic unit costs')\n",
    "    model.var('omegat', desc='Target real wage for workers')\n",
    "    model.var('P', desc='Price level')\n",
    "    model.var('Pbl', desc='Price of government bonds')\n",
    "    model.var('PI', desc='Price inflation')\n",
    "    model.var('PSBR', desc='Government deficit')\n",
    "    model.var('Ra', desc='Interest rate on Central bank advances')\n",
    "    model.var('Rb', desc='Interest rate on government bills')\n",
    "    model.var('Rbl', desc='Interest rate on bonds')\n",
    "    model.var('Rl', desc='Interest rate on loans')\n",
    "    model.var('Rm', desc='Interest rate on deposits')\n",
    "    model.var('RRb', desc='Real interest rate on bills')\n",
    "    model.var('RRbl', desc='Real interest rate on long term bonds')\n",
    "    model.var('RRl', desc='Real interest rate on loans')\n",
    "    model.var('RRm', desc='Real interest rate on term deposits')\n",
    "    model.var('S', desc='Sales at current prices')\n",
    "    model.var('Sk', desc='Real sales')\n",
    "    model.var('Ske', desc='Expected real sales')\n",
    "    model.var('sigmas', desc='Realized inventories to sales ratio')\n",
    "    model.var('sigmat', desc='Target inventories to sales ratio')\n",
    "    model.var('T', desc='Taxes')\n",
    "    model.var('UC', desc='Unit costs')\n",
    "    model.var('V', desc='Wealth of households')\n",
    "    model.var('Ve', desc='Expected household wealth')\n",
    "    model.var('Vk', desc='Real wealth of households')\n",
    "    model.var('Vnc', desc='Wealth of households, net cash')\n",
    "    model.var('Vnce', desc='Expected wealth of households, net cash')\n",
    "    model.var('WB', desc='The wage bill')\n",
    "    model.var('Y', desc='Output at current prices')\n",
    "    model.var('Yk', desc='Real output')\n",
    "    model.var('YDhs', desc='Haig-Simons measure of disposable income')\n",
    "    model.var('YDkhs', desc='Haig-Simons measure of real disposable income')\n",
    "    model.var('YDkr', desc='Regular real disposable income')\n",
    "    model.var('YDkre', desc='Expected regular real disposable income')\n",
    "    model.var('YDr', desc='Regular disposable income')\n",
    "    model.var('YDre', desc='Expected regular disposable income')\n",
    "    \n",
    "\n",
    "    model.set_param_default(0)\n",
    "    model.param('alpha0', desc='Autonomous consumption')\n",
    "    model.param('alpha1', desc='Propensity to consume out of income')\n",
    "    model.param('alpha2', desc='Propensity to consume out of wealth')\n",
    "    model.param('beta', desc='Parameter in expectation formations on real sales')\n",
    "    model.param('bot', desc='Bottom value for bank net liquidity ratio')\n",
    "    model.param('botpm', desc='Bottom value for bank profit margin')\n",
    "    model.param('eps', desc='Parameter in expectation formations on real disposable income')\n",
    "    model.param('gamma', desc='Speed of adjustment of inventories to the target level')\n",
    "    model.param('lambda20', desc='Parameter in household demand for time deposits')\n",
    "    model.param('lambda21', desc='Parameter in household demand for time deposits')\n",
    "    model.param('lambda22', desc='Parameter in household demand for time deposits')\n",
    "    model.param('lambda23', desc='Parameter in household demand for time deposits')\n",
    "    model.param('lambda24', desc='Parameter in household demand for time deposits')\n",
    "    model.param('lambda25', desc='Parameter in household demand for time deposits')\n",
    "    model.param('lambda30', desc='Parameter in household demand for bills')\n",
    "    model.param('lambda31', desc='Parameter in household demand for bills')\n",
    "    model.param('lambda32', desc='Parameter in household demand for bills')\n",
    "    model.param('lambda33', desc='Parameter in household demand for bills')\n",
    "    model.param('lambda34', desc='Parameter in household demand for bills')\n",
    "    model.param('lambda35', desc='Parameter in household demand for bills')\n",
    "    model.param('lambda40', desc='Parameter in household demand for bonds')\n",
    "    model.param('lambda41', desc='Parameter in household demand for bonds')\n",
    "    model.param('lambda42', desc='Parameter in household demand for bonds')\n",
    "    model.param('lambda43', desc='Parameter in household demand for bonds')\n",
    "    model.param('lambda44', desc='Parameter in household demand for bonds')\n",
    "    model.param('lambda45', desc='Parameter in household demand for bonds')\n",
    "    model.param('lambdac', desc='Parameter in household demand for cash')\n",
    "    model.param('phi', desc='Mark-up on unit costs')\n",
    "    model.param('ro1', desc='Reserve requirements parameter')\n",
    "    model.param('ro2', desc='Reserve requirements parameter')\n",
    "    model.param('sigma0', desc='Parameter determining the target inventories to sales ratio')\n",
    "    model.param('sigma1', desc='Parameter linking the target inventories to sales ratio to the interest rate')\n",
    "    model.param('tau', desc='Sales tax rate')\n",
    "    model.param('top', desc='Top value for bank net liquidity ratio')\n",
    "    model.param('toppm', desc='Top value for bank profit margin')\n",
    "    \n",
    "    model.var('z1', desc='Is 1 if bank checking accounts are non-negative')\n",
    "    model.var('z2', desc='Is 1 if bank checking accounts are negative')\n",
    "    model.var('z3', desc='Is 1 if banks net liquidity ratio is below bottom level')\n",
    "    model.var('z4', desc='Is 1 if banks net liquidity ratio was below bottom level')\n",
    "    model.var('z4b', desc='Is 1 if banks net liquidity ratio was way below bottom level')\n",
    "    model.var('z5', desc='Is 1 if banks net liquidity ratio was above top level')\n",
    "    model.var('z5b', desc='Is 1 if banks net liquidity ratio was way above top level')\n",
    "    model.var('z6', desc='Is 1 if banks profit margin is below bottom level')\n",
    "    model.var('z7', desc='Is 1 if banks profit margin is above top level')\n",
    "    \n",
    "    model.param('xib', desc='Parameter in the equation for setting interest rate on deposits')\n",
    "    model.param('xil', desc='Parameter in the equation for setting interest rate on loans')\n",
    "    model.param('xim', desc='Parameter in the equation for setting interest rate on deposits')\n",
    "    model.param('omega0', desc='Parameter influencing the target real wage for workers')\n",
    "    model.param('omega1', desc='Parameter influencing the target real wage for workers')\n",
    "    model.param('omega2', desc='Parameter influencing the target real wage for workers')\n",
    "    model.param('omega3', desc='Speed of adjustment of wages to target value')\n",
    "    \n",
    "    model.param('ERrbl', desc='Expected rate of return on long term bonds')\n",
    "    model.param('Gk', desc='Real government expenditures')\n",
    "    model.param('Nfe', desc='Full employment level')\n",
    "    model.param('PR', desc='Labour productivity')\n",
    "    model.param('Rbbar', desc='Interest rate on bills, set exogenously')\n",
    "    model.param('Rblbar', desc='Interest rate on bonds, set exogenously')\n",
    "    \n",
    "    model.var('W', desc='Wage rate')\n",
    "\n",
    "    # Box 10.1 Firms' decisions\n",
    "    # -------------------------\n",
    "    \n",
    "    model.add('Yk = Ske + INke - INk(-1)')          # 10.1 : Real output\n",
    "    model.add('N = Yk/PR')                          # 10.2 : Employment\n",
    "    model.add('WB = N*W')                           # 10.3 : The wage bill\n",
    "    model.add('UC = WB/Yk')                         # 10.4 : Unit costs\n",
    "    model.add('Ske = beta*Sk(-1) + (1-beta)*Ske(-1)') # 10.5 : Expected real sales\n",
    "    model.add('INkt = sigmat * Ske')                # 10.6 : Target level of real inventories    \n",
    "    model.add('sigmat = sigma0 - sigma1*Rl')       # 10.7 : Target inventories to sales ratio\n",
    "    model.add('RRl = (1 + Rl)/(1 + PI) - 1')       # 10.8 : Real interest rate on loans\n",
    "    model.add('INke = INk(-1) + gamma*(INkt - INk(-1))') # 10.9 : Expected real inventories\n",
    "    model.add('NHUC = (1 - sigmat)*UC + sigmat*(1 + Rl(-1))*UC(-1)') # 10.11 : Normal historic unit costs\n",
    "    model.add('P = (1 + tau)*(1 + phi)*NHUC')       # 10.10 : Price level\n",
    "    model.add('Ffe = (phi/(1+phi))*(1/(1+tau))*P*Ske') # 10.11A : Expected profits of firms\n",
    "\n",
    "    # Box 10.2 : Firms' equations\n",
    "    # ---------------------------\n",
    "    model.add('Sk = Ck + Gk')                       # 10.12 : Real sales\n",
    "    model.add('S = P * Sk')                         # 10.13 : Sales at current prices\n",
    "    model.add('INk - INk(-1) = Yk - Sk')            # 10.14 : Real inventories\n",
    "    model.add('sigmas = INk(-1)/Sk')                # 10.15 : Realized inventories to sales ratio\n",
    "    model.add('IN = INk*UC')                        # 10.16 : Stock of inventories\n",
    "    model.add('Ld = IN')                            # 10.17 : Demand for loans\n",
    "    model.add('Ff = S - T - WB + IN - IN(-1) - Rl(-1)*IN(-1)') # 10.18 : Firms realized profits\n",
    "    model.add('PI = P/P(-1) - 1')                   # 10.19 : Rate of price inflation\n",
    "\n",
    "    # Box 10.3 : Household equations\n",
    "    # ------------------------------\n",
    "    model.add('YDr = WB + F + Rm(-1)*M2d(-1) + Rb(-1)*Bhh(-1) + BLh(-1)') # 10.20 : Regular disposable income\n",
    "    model.add('CG = (Pbl - Pbl(-1))*BLh(-1)')       # 10.21 : Capital gains on bonds\n",
    "    model.add('YDhs = YDr + CG')                    # 10.22 : Haig-Simons measure of disposable income\n",
    "    model.add('F = Ff + Fb')                        # 10.23 : Total net profits\n",
    "    model.add('V = V(-1) + YDhs - CONS')            # 10.24 : Nominal wealth\n",
    "    model.add('Vnc = V - Hhd')                      # 10.25 : Nominal wealth net of cash\n",
    "    model.add('YDkr = (YDr - PI*V(-1))/P')          # 10.26 : Real regular disposable income\n",
    "    model.add('YDkhs = (YDr - PI*V(-1) + CG)/P')    # 10.27 : Real HS disposable income\n",
    "    model.add('Vk = V/P')                           # 10.28 : Real wealth of households\n",
    "\n",
    "    # Box 10.4 : Household equations\n",
    "    # ------------------------------\n",
    "    model.add('Ck = alpha0 + alpha1*YDkre + alpha2*Vk(-1)') # 10.29 : Consumption decision\n",
    "    model.add('YDkre = eps*YDkr(-1) + (1 - eps)*YDkre(-1)') # 10.30 : Expected real regular disposable income\n",
    "    model.add('CONS = Ck*P')                        # 10.31 : Consumption at current prices\n",
    "    model.add('YDre = P*YDkre + PI*V(-1)/P')        # 10.32 : Expected regular disposable income\n",
    "    model.add('Ve = V(-1) + YDre - CONS')           # 10.33 : Expected nominal wealth\n",
    "    model.add('Hhd = lambdac*CONS')                 # 10.34 : Household demand for cash\n",
    "    model.add('Vnce = Ve - Hhd')                    # 10.35 : Expected nominal wealth net of cash\n",
    "\n",
    "    # Box 10.5 : Households portfolio equations, based on nominal rates\n",
    "    # -----------------------------------------------------------------\n",
    "    # 10.37 : Demand for term banks deposit\n",
    "    model.add('M2d = (lambda20 + lambda22*Rm + lambda23*Rb + lambda24*ERrbl + lambda25*(YDre/Vnce))*Vnce')\n",
    "    # 10.38 : Demand for government bills\n",
    "    model.add('Bhd = (lambda30 + lambda32*Rm + lambda33*Rb + lambda34*ERrbl + lambda35*(YDre/Vnce))*Vnce')\n",
    "    # 10.39 : Demand for government bonds\n",
    "    model.add('BLd = (lambda40 + lambda42*Rm + lambda43*Rb + lambda44*ERrbl + lambda45*(YDre/Vnce))*Vnce/Pbl')\n",
    "\n",
    "    # Box 10.6 : Households portfoloio equations, based on real rates\n",
    "    # ---------------------------------------------------------------\n",
    "    # 10.37A : \"Notional\" Demand for term banks deposits\n",
    "    #   M2d = (lambda20 - lambda21*PI/(1 + PI) + lambda22*RRm + lambda23*RRb + lambda24*RRbl + lambda25*YDre/Vnce))*Vnce\n",
    "    # 10.38A : Demand for government bills\n",
    "    #   Bhd = (lambda30 - lambda31*PI/(1 + PI) + lambda32*RRm + lambda33*RRb + lambda34*RRbl + lambda35*YDre/Vnce))*Vnce\n",
    "    # 10.39A : Demand for government bonds\n",
    "    #   BLd = (lambda40 - lambda41*PI/(1 + PI) + lambda42*RRm + lambda43*RRb + lambda44*RRbl + lambda45*YDre/Vnce))*Vnce/PIbl\n",
    "\n",
    "    model.add('RRm = (1 + Rm)/(1 + PI) - 1')        # 10.37B : Real interest rate on term deposits\n",
    "    model.add('RRb = (1  + Rb)/(1+ PI) - 1')        # 10.38B : Real interest rate on bills\n",
    "    model.add('RRbl = (1 + Rbl)/(1 + PI) - 1')      # 10.39B : Real interest rate on long-term bonds\n",
    "\n",
    "    # Box 10.7 : Households equations, realized portfolio asset holding\n",
    "    # -----------------------------------------------------------------\n",
    "    model.add('Hhh = Hhd')                          # 10.40 : Cash holding\n",
    "    model.add('Bhh = Bhd')                          # 10.41 : Holding of bills\n",
    "    model.add('BLh = BLd')                          # 10.42 : Holding of bonds\n",
    "    model.add('M1hn = Vnc - M2d - Bhd - Pbl*BLd')   # 10.43 : Notional holding of bank checking accounts\n",
    "    model.add('M1h = M1hn * z1')                    # 10.44 : Holding of bank checking accounts\n",
    "    model.add('z1 = if_true(M1hn >= 0)')            # 10.45 : Condition for non-negative bank checking acounts\n",
    "    model.add('M2h = M2d*z1 + (Vnc - Bhh - Pbl*BLd)*z2') # 10.46 : Holding of bank term deposits\n",
    "    model.add('z2 = 1 - z1')                        # 10.47 : Condition for negative bank checking accounts\n",
    "\n",
    "    # Box 10.8 : Government equations\n",
    "    # -------------------------------\n",
    "    model.add('T = S*tau/(1 + tau)')                # 10.48 : Tax receipts\n",
    "    model.add('G = P*Gk')                           # 10.49 : Government expenditures\n",
    "    model.add('PSBR = G + Rb(-1)*Bs(-1) + BLs(-1) - (T + Fcb)') # 10.50 : Government deficit\n",
    "    model.add('Bs - Bs(-1) = PSBR - (BLs - BLs(-1))*Pbl') # 10.51 : New issues of bills\n",
    "    model.add('BLs = BLd')                          # 10.52 : Supply of bonds\n",
    "    model.add('Pbl = 1/Rbl')                        # 10.53 : Price of bonds\n",
    "    model.add('Rbl = Rblbar + PI(-1)')              # 10.54 : Yield on bonds is exogenous\n",
    "\n",
    "    # Box 10.9 : Central bank equations\n",
    "    # ---------------------------------\n",
    "    model.add('Hs = Bcb + As')                      # 10.55 : Supply of cash\n",
    "    model.add('Hbs = Hs - Hhs')                     # 10.56 : Supply of cash to commercial banks\n",
    "    model.add('Bcb = Bs - Bhh - Bbd')               # 10.57 : CB purchases of government bills\n",
    "    model.add('Rb = Rbbar + PI(-1)')                # 10.58 : Interest rate on government bills, set exogenously\n",
    "    model.add('As = Ad')                            # 10.59 : Supply of CB advances to commercial banks\n",
    "    model.add('Ra = Rb')                            # 10.60 : Interest rate on CB advances\n",
    "    model.add('Fcb = Rb(-1)*Bcb(-1) + Ra(-1)*As(-1)') # 10.61 : Profits of Central Bank\n",
    "\n",
    "    # Box 10.10 : Commercial bank equations\n",
    "    # -------------------------------------\n",
    "    model.add('Hhs = Hhd')                          # 10.62 : Supply of cash to households\n",
    "    model.add('M1s = M1h')                          # 10.63 : Supply of checking deposits\n",
    "    model.add('M2s = M2d')                          # 10.64 : Supply of time deposits\n",
    "    model.add('Ls = Ld')                            # 10.65 : Supply of loans\n",
    "    model.add('Hbd = ro1*M1s + ro2*M2s')            # 10.66 : Demand for cash by banks (reserve requirement)\n",
    "\n",
    "    # Box 10.11 : Commercial bank equations\n",
    "    # -------------------------------------\n",
    "    model.add('Bbdn = M1s + M2s - Ls - Hbd')        # 10.67 : Notional demand for bills\n",
    "    model.add('BLRn = Bbdn/(M1s + M2s)')             # 10.68 : Net bank liquidity ratio\n",
    "    model.add('Ad = (bot*(M1s + M2s) - Bbdn)*z3')   # 10.69 : Advances needed by banks\n",
    "    model.add('z3 = if_true(BLRn < bot)')           # 10.70 : Check if net liquidity is above bottom value\n",
    "    model.add('Bbd = Ad + M1s + M2s - Ls - Hbd')    # 10.71 : Demand for government bills\n",
    "    model.add('BLR = Bbd/(M1s + M2s)')              # 10.72 : Gross bank liquidity ratio\n",
    "\n",
    "    # Box 10.12 : Commercial bank equations\n",
    "    # -------------------------------------\n",
    "    # 10.73 : Interest rate on deposits\n",
    "    model.add('Rm = Rm(-1) + 0.0001*z4 + 0.0002*z4b - 0.0001*z5 - 0.0002*z5b + xib*(Rb - Rb(-1))')\n",
    "    model.add('z4 = if_true(BLRn(-1) < bot)')       # 10.75 : Check if net liquidity ratio was below bottom value\n",
    "    model.add('z4b = if_true(BLRn(-1) < (bot - 0.02))')\n",
    "    model.add('z5 = if_true(BLRn(-1) > top)')        # 10.76 : Check if net liquidity ratio was above top value\n",
    "    model.add('z5b = if_true(BLRn(-1) > (top+0.02))')\n",
    "    # 10.77 : Realized bank profits\n",
    "    model.add('Fb = Rl(-1)*Ls(-1) + Rb(-1)*Bbd(-1) - Rm(-1)*M2s(-1) - Ra(-1)*Ad(-1)')\n",
    "    model.add('Rl - Rl(-1) = xil*(z6 - z7) + (Rb - Rb(-1))') # 10.78 : Interest rate on loans\n",
    "    model.add('z6 = if_true(BPM < botpm)')          # 10.80 : Check if banks profit margin is below bottom value\n",
    "    model.add('z7 = if_true(BPM > toppm)')          # 10.81 : Check if banks profit margin is above top value\n",
    "    model.add('BPM = (Fb + Fb(-1))/(M1s(-1) + M1s(-2) + M2s(-1) + M2s(-2))') # 10.82 : Banks profit margin\n",
    "\n",
    "    # Inflationary forces\n",
    "    # -------------------\n",
    "    # 10.84 : Target real wage for workers\n",
    "    model.add('omegat = exp(omega0 + omega1*log(PR) + omega2*log((N/Nfe)))') \n",
    "    model.add('W = W(-1)*(1 + omega3*(omegat(-1) - W(-1)/P(-1)))') # 10.85 Unit wages\n",
    "\n",
    "    # Addtional equations\n",
    "    # -------------------\n",
    "    model.add('Y = P*Sk + (INk - INk(-1))*UC')      # Output at current prices\n",
    "\n",
    "    return model\n",
    "\n",
    "insoutb_parameters = {'alpha0': 0,\n",
    "                     'alpha1': 0.95,\n",
    "                     'alpha2': 0.05,\n",
    "                     'beta': 0.5,\n",
    "                     'bot': 0.02,\n",
    "                     'botpm': 0.002,\n",
    "                     'eps': 0.5,\n",
    "                     'gamma': 0.5,\n",
    "                     'lambda20': 0.52245,\n",
    "                     'lambda21': 20,\n",
    "                     'lambda22': 40,\n",
    "                     'lambda23': -20,\n",
    "                     'lambda24': -20,\n",
    "                     'lambda25': -0.06,\n",
    "                     'lambda30': 0.47311,\n",
    "                     'lambda31': 40,\n",
    "                     'lambda32': -20,\n",
    "                     'lambda33': 40,\n",
    "                     'lambda34': -20,\n",
    "                     'lambda35': -0.06,\n",
    "                     'lambda40': 0.17515,\n",
    "                     'lambda41': 20,\n",
    "                     'lambda42': -20,\n",
    "                     'lambda43': -20,\n",
    "                     'lambda44': 40,\n",
    "                     'lambda45': -0.06,\n",
    "                     'lambdac': 0.1,\n",
    "                     'phi': 0.1,\n",
    "                     'ro1': 0.1,\n",
    "                     'ro2': 0.1,\n",
    "                     'sigma0': 0.3612,\n",
    "                     'sigma1': 3,\n",
    "                     'tau': 0.25,\n",
    "                     'top': 0.04,\n",
    "                     'toppm': 0.005,\n",
    "                     'xib': 0.9,\n",
    "                     'xil': 0.002,\n",
    "                     'xim': 0.0002,\n",
    "                     'omega0': -0.32549,\n",
    "                     'omega1': 1,\n",
    "                     'omega2': 1.5,\n",
    "                     'omega3': 0.1}\n",
    "insoutb_exogenous = {'Gk': 25,\n",
    "                    'Nfe': 133.28,\n",
    "                    'PR': 1,\n",
    "                    'Rbbar': 0.023,\n",
    "                    'Rblbar': 0.027,\n",
    "                    'ERrbl': 0.027,\n",
    "                    'W': 1}\n",
    "insoutb_variables = [('Bbd', 1.19481),\n",
    "                    ('Bbdn', 1.19481),\n",
    "                    ('Bcb', 19.355),\n",
    "                    ('Bhh', 49.69136),\n",
    "                    ('Bhd', 'Bhh'),\n",
    "                    ('Bs', 70.24123),\n",
    "                    ('BLh', 1.12309),\n",
    "                    ('BLd', 'BLh'),\n",
    "                    ('BLs', 'BLd'),\n",
    "                    ('Hbd', 4.36249),\n",
    "                    ('Hbs', 'Hbd'),\n",
    "                    ('Hhd', 14.992),\n",
    "                    ('Hhh', 'Hhd'),\n",
    "                    ('Hhs', 'Hhd'),\n",
    "                    ('INk', 38.07),\n",
    "                    ('INke', 'INk'),\n",
    "                    ('IN', 38.0676),\n",
    "                    ('Ls', 38.0676),\n",
    "                    ('Ld', 'Ls'),\n",
    "                    ('M1s', 3.9482),\n",
    "                    ('M1h', 'M1s'),\n",
    "                    ('M1hn', 'M1s'),\n",
    "                    ('M2s', 39.667),\n",
    "                    ('M2d', 'M2s'),\n",
    "                    ('M2h', 'M2d'),\n",
    "                    ('Vk', 108.285),\n",
    "                    \n",
    "                    ('Ra', 0.02301),\n",
    "                    ('Rb', 0.02301),\n",
    "                    ('Rl', 0.02515),\n",
    "                    ('Rm', 0.02095),\n",
    "                    ('BLRn', 0.02737),\n",
    "                    ('Fb', 0.1535),\n",
    "                    ('P', 1.38469),\n",
    "                    ('Pbl', 37.06),\n",
    "                    ('Rbl', 'Rblbar'),\n",
    "                    ('Sk', 133.277),\n",
    "                    ('Ske', 'Sk'),\n",
    "                    ('UC', 1),\n",
    "                    ('YDkr', 108.28),\n",
    "                    ('YDkre', 108.28),\n",
    "                    ('V', 'Vk*P'),\n",
    "                    ('Ve' , 'V'),\n",
    "                    ('Vnc', 'V - Hhh'),\n",
    "                    ('Vnce', 'Vnc'),\n",
    "                    ('omegat', 0.72215)]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scenario: Model INOUTSB, Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "baseline = create_insoutb_model()\n",
    "baseline.set_values(insoutb_parameters)\n",
    "baseline.set_values(insoutb_exogenous)\n",
    "baseline.set_values(insoutb_variables)\n",
    "\n",
    "# run to convergence\n",
    "# Give the system more time to reach a steady state\n",
    "for _ in range(65):\n",
    "    baseline.solve(iterations=200, threshold=1e-6)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scenario: Model INSOUTB, increase in target real wage rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "omega0 = create_insoutb_model()\n",
    "omega0.set_values(insoutb_parameters)\n",
    "omega0.set_values(insoutb_exogenous)\n",
    "omega0.set_values(insoutb_variables)\n",
    "\n",
    "for _ in range(15):\n",
    "    omega0.solve(iterations=200, threshold=1e-6)\n",
    "\n",
    "omega0.set_values({'omega0': -0.28})\n",
    "\n",
    "for _ in range(50):\n",
    "    omega0.solve(iterations=200, threshold=1e-6)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 10.7A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "caption = '''\n",
    "    Figure 10.7A  Evolution of real sales following a one-step increase in the target real wage \n",
    "    that generates an increase in the rate of inflation, accompanied by an increase in the \n",
    "    nominal interest rate the approximately compensates for the increase in inflation.'''\n",
    "skdata = [s['Sk'] for s in omega0.solutions[5:]]\n",
    "\n",
    "fig = plt.figure()\n",
    "axes = fig.add_axes([0.1, 0.1, 1.1, 1.1])\n",
    "axes.tick_params(top='off', right='off')\n",
    "axes.spines['top'].set_visible(False)\n",
    "axes.spines['right'].set_visible(False)\n",
    "\n",
    "axes.plot(skdata, linestyle='-', color='b')\n",
    "\n",
    "# add labels\n",
    "plt.text(20, 132.6, 'Real sales')\n",
    "fig.text(0.1, -.1, caption);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 10.7B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "caption = '''\n",
    "    Figure 10.7B  Evolution of real household debt and real government debt following\n",
    "    a one-step increase in the target real wage that generates an increase in the\n",
    "    rate of inflation, accompanied by an increase in nominal interest rates that\n",
    "    approximately compensates for the increase in inflation.'''\n",
    "wdata = [(s['Bs'] + s['BLs']*s['Pbl'])/s['P'] for s in omega0.solutions[5:]]\n",
    "vkdata = [s['Vk'] for s in omega0.solutions[5:]]\n",
    "\n",
    "fig = plt.figure()\n",
    "axes = fig.add_axes([0.1, 0.1, 1.1, 1.1])\n",
    "axes.tick_params(top='off', right='off')\n",
    "axes.spines['top'].set_visible(False)\n",
    "axes.spines['right'].set_visible(False)\n",
    "axes.set_ylim(65, 115)\n",
    "\n",
    "axes.plot(vkdata, linestyle='-', color='g')\n",
    "axes.plot(wdata, linestyle='-', color='b')\n",
    "\n",
    "# add labels\n",
    "plt.text(20, 82, 'Real government debt')\n",
    "plt.text(20, 109, 'Real household wealth')\n",
    "fig.text(0.1, -.15, caption);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 10.7C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "caption = '''\n",
    "    Figure 10.7C  Evolution of the deflated government baance, adjusted and\n",
    "    unadjusted for inflation gains, following a one-step increase in the target\n",
    "    real wage that generates an increase in the rate of inflation, accompanied\n",
    "    by an increase in nominal interest rates that approximately compenstates for\n",
    "    the increase in inflatio.'''\n",
    "\n",
    "psbrdata = list()\n",
    "data = list()\n",
    "for i in range(5, len(omega0.solutions)):\n",
    "    s7 = omega0.solutions[i]\n",
    "    s7_1 = omega0.solutions[i-1]\n",
    "    s0 = baseline.solutions[i]\n",
    "    s0_1 = baseline.solutions[i-1]\n",
    "   \n",
    "    psbrdata.append((s0['PSBR']/s0['P']) - (s7['PSBR']/s7['P']))\n",
    "    data.append((-s7['PSBR'] + (s7['P'] - s7_1['P'])*(s7_1['Bs']+s7_1['BLs']*s7_1['Pbl'])/s7['P']) -\n",
    "                (-s0['PSBR'] + (s0['P'] - s0_1['P'])*(s0_1['Bs']+s0_1['BLs']*s0_1['Pbl'])/s0['P']))\n",
    "\n",
    "fig = plt.figure()\n",
    "axes = fig.add_axes([0.1, 0.1, 1.1, 1.1])\n",
    "axes.tick_params(top='off', right='off')\n",
    "axes.spines['top'].set_visible(False)\n",
    "axes.spines['right'].set_visible(False)\n",
    "#axes.set_ylim(-0.5, 1)\n",
    "\n",
    "axes.plot(psbrdata, color='b')\n",
    "axes.plot(data, linestyle='-', color='g')\n",
    "\n",
    "# add labels\n",
    "plt.text(17, 0.2, 'Real government budget balance')\n",
    "plt.text(17, 0.17, '(adjusted for inflation gains)')\n",
    "plt.text(21, -0.19, 'Real government budget balance')\n",
    "plt.text(21, -0.22, '(unadjusted for inflation gains)')\n",
    "fig.text(0.1, -.2, caption);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}