Chapter 11 Model GROWTHB.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Monetary Economics: Chapter 11"
   ]
  },
  {
   "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 GROWTHB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_growthb_model():\n",
    "    model = Model()\n",
    "        \n",
    "    model.set_var_default(0)\n",
    "    model.var('ADDl', desc='Spread between interest rate on loans and rate on deposits')\n",
    "    model.var('Bbd', desc='Government bills demanded by commercial banks')\n",
    "    model.var('Bbs', desc='Government bills supplied to commercial banks')\n",
    "    model.var('Bcbd', desc='Government bills demanded by Central bank')\n",
    "    model.var('Bcbs', desc='Government bills supplied by Central bank')\n",
    "    model.var('Bhd', desc='Demand for government bills from households')\n",
    "    model.var('Bhs', desc='Government bills supplied to households')\n",
    "    model.var('Bs', desc='Supply of government bills')\n",
    "    model.var('BLd', 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('BUR', desc='Burden of personal debt')\n",
    "    model.var('Ck', desc='Real consumption')\n",
    "    model.var('CAR', desc='Capital adequacy ratio of banks')\n",
    "    model.var('CG', desc='Capital gains on government bonds')\n",
    "    model.var('CONS', desc='Consumption at current prices')\n",
    "    model.var('Ekd', desc='Number of equities demanded')\n",
    "    model.var('Eks', desc='Number of equities supplied by firms')\n",
    "    model.var('ER', desc='Employment rate')\n",
    "    model.var('Fb', desc='Realized banks profits')\n",
    "    model.var('Fbt', desc='Target profits of banks')\n",
    "    model.var('Fcb', desc='Central bank \"profits\"')\n",
    "    model.var('Ff', desc='Realized entrepreneurial profits')\n",
    "    model.var('Fft', desc='Planned entrepreneurial profits')\n",
    "    model.var('FDb', desc='Dividends of banks')\n",
    "    model.var('FDf', desc='Dividends of firms')\n",
    "    model.var('FUb', desc='Retained earnings of banks')\n",
    "    model.var('FUbt', desc='Targt retained earnings of banks')\n",
    "    model.var('FUf', desc='Retained earnings of firms')\n",
    "    model.var('FUft', desc='Planned retained earnings of firms')\n",
    "    model.var('G', desc='Government expenditures')\n",
    "    model.var('Gk', desc='Real government expenditures')\n",
    "    model.var('GD', desc='Government debt')\n",
    "    model.var('GL', desc='Gross amount of new personal loans')\n",
    "    model.var('GRk', desc='growth_mod of real capital stock')\n",
    "    model.var('Hbd', desc='Cash required by banks')\n",
    "    model.var('Hbs', desc='Cash supplied to banks')\n",
    "    model.var('Hhd', desc='Households demand for cash')\n",
    "    model.var('Hhs', desc='Cash supplied to households')\n",
    "    model.var('Hs', desc='Total supply of cash')\n",
    "    model.var('HCe', desc='Expected historical costs')\n",
    "    model.var('INV', desc='Gross investment')\n",
    "    model.var('Ik', desc='Gross investment in real terms')\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('K', desc='Capital stock')\n",
    "    model.var('Kk', desc='Real capital stock')\n",
    "    model.var('Lfd', desc='Demand for loans by firms')\n",
    "    model.var('Lfs', desc='Supply of loans to firms')\n",
    "    model.var('Lhd', desc='Demand for loans by households')\n",
    "    model.var('Lhs', desc='Loans supplied to households')\n",
    "    model.var('Md', desc='Deposits demanded by households')\n",
    "    model.var('Ms', desc='Deposits supplied by banks')\n",
    "    model.var('N', desc='Employment level')\n",
    "    model.var('Nt', desc='Desired employment level')\n",
    "    model.var('NHUC', desc='Normal historic unit cost')\n",
    "    model.var('NL', desc='Net flow of new loans to the household sector')\n",
    "    model.var('NLk', desc='Real flow of new loans to the household sector')\n",
    "    model.var('NPL', desc='Non-Performing loans')\n",
    "    model.var('NPLke', desc='Expected proportion of Non-Performing Loans')\n",
    "    model.var('NUC', desc='Normal unit cost')\n",
    "    model.var('OFb', desc='Own funds of banks')\n",
    "    model.var('OFbe', desc='Short-run target for banks own funds')\n",
    "    model.var('OFbt', desc='Long-run target for banks own funds')\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('Pe', desc='Price of equities')\n",
    "    model.var('PE', desc='Price earnings ratio')\n",
    "    model.var('PI', desc='Price inflation')\n",
    "    model.var('PR', desc='Lobor productivity')\n",
    "    model.var('PSBR', desc='Government deficit')\n",
    "    model.var('Q', desc=\"Tobin's Q\")\n",
    "    model.var('Rb', desc='Interest rate on government bills')\n",
    "    model.var('Rbl', desc='Interest rate on bonds')\n",
    "    model.var('Rk', desc='Dividend yield of firms')\n",
    "    model.var('Rl', desc='Interest rate on loans')\n",
    "    model.var('Rm', desc='Interest rate on deposits')\n",
    "    model.var('REP', desc='Personal loans repayments')\n",
    "    model.var('RRl', desc='Real interest rate on loans')\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('T', desc='Taxes')\n",
    "    model.var('U', desc='Capital utilization proxy')\n",
    "    model.var('UC', desc='Unit costs')\n",
    "    model.var('V', desc='Wealth of households')\n",
    "    model.var('Vk', desc='Real wealth of households')\n",
    "    model.var('Vfma', desc='Investible wealth of households')\n",
    "    model.var('W', desc='Wage rate')\n",
    "    model.var('WB', desc='The wage bill')\n",
    "    model.var('Y', desc='Output at current prices (nominal GDP)')\n",
    "    model.var('Yk', desc='Real output')\n",
    "    model.var('YDhs', desc='Haig-Simons measure of disposable income')\n",
    "    model.var('YDr', desc='Regular disposable income')\n",
    "    model.var('YDkr', desc='Regular real disposable income')\n",
    "    model.var('YDkre', desc='Expected regular real disposable income')\n",
    "    model.var('YP', desc='Personal income')\n",
    "\n",
    "    model.var('RRb', desc='Real interest rate on bills')\n",
    "    model.var('RRbt', desc='Target real interest rate on bills')\n",
    "\n",
    "    model.var('eta', desc='Ratio of new loans to personal income')\n",
    "    model.var('phi', desc='Mark-up on unit costs')\n",
    "    model.var('phit', desc='Ideal mark-up on unit costs')\n",
    "    model.var('z1a', desc='Is one if bank liquidity ratio is below bottom range')\n",
    "    model.var('z1b', desc='Is one if bank liquidity ratio is below bottom range')\n",
    "    model.var('z2a', desc='Is one if bank liquidity ratio is above top range')\n",
    "    model.var('z2b', desc='Is one if bank liquidity ratio is above top range')\n",
    "    model.var('z3', desc='Parameter in wage aspiration equation')\n",
    "    model.var('z4', desc='Parameter in wage aspiration equation')\n",
    "    model.var('z5', desc='Parameter in wage aspiration equation')\n",
    "    model.var('sigmase', desc='Opening inventories to expected sales ratio')\n",
    "    \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('betab', desc='Spped of adjustment of banks own funds')\n",
    "    model.param('bot', desc='Bottom value for bank net liquidity ratio')\n",
    "    model.param('delta', desc='Rate of depreciation of fixed capital')\n",
    "    model.param('deltarep', desc='Ratio of personal loans repayments to stock of loans')\n",
    "    model.param('eps', desc='Parameter in expectation formations on real disposable income')\n",
    "    model.param('eps2', desc='Speed of adjustment of mark-up')\n",
    "    model.param('epsb', desc='Speed of adjustment in expected proportion of non-performing loans')\n",
    "    model.param('epsrb', desc='Speed of adjustment in the real interest rate on bills')\n",
    "    model.param('eta0', desc='Ratio of new loans to personal income - exogenous component')\n",
    "    model.param('etan', desc='Speed of adjustment of actual employment to desired employment')\n",
    "    model.param('etar', desc='Relation between the ratio of new loans to personal income and the interest rate on loans')\n",
    "    model.param('gamma', desc='Speed of adjustment of inventories to the target level')\n",
    "    model.param('gamma0', desc='Exogenous growth_mod in the real stock of capital')\n",
    "    model.param('gammar', desc='Relation between the real interest rate and growth_mod in the stock of capital')\n",
    "    model.param('gammau', desc='Relation between the utilization rate and growth_mod in the stock of capital')\n",
    "    model.param('lambda20', desc='Parameter in households demand for bills')\n",
    "    model.param('lambda21', desc='Parameter in households demand for bills')\n",
    "    model.param('lambda22', desc='Parameter in households demand for bills')\n",
    "    model.param('lambda23', desc='Parameter in households demand for bills')\n",
    "    model.param('lambda24', desc='Parameter in households demand for bills')\n",
    "    model.param('lambda25', desc='Parameter in households demand for bills')\n",
    "    model.param('lambda30', desc='Parameter in households demand for bonds')\n",
    "    model.param('lambda31', desc='Parameter in households demand for bonds')\n",
    "    model.param('lambda32', desc='Parameter in households demand for bonds')\n",
    "    model.param('lambda33', desc='Parameter in households demand for bonds')\n",
    "    model.param('lambda34', desc='Parameter in households demand for bonds')\n",
    "    model.param('lambda35', desc='Parameter in households demand for bonds')\n",
    "    model.param('lambda40', desc='Parameter in households demand for equities')\n",
    "    model.param('lambda41', desc='Parameter in households demand for equities')\n",
    "    model.param('lambda42', desc='Parameter in households demand for equities')\n",
    "    model.param('lambda43', desc='Parameter in households demand for equities')\n",
    "    model.param('lambda44', desc='Parameter in households demand for equities')\n",
    "    model.param('lambda45', desc='Parameter in households demand for equities')\n",
    "    model.param('lambdab', desc='Parameter determining dividends of banks')\n",
    "    model.param('lambdac', desc='Parameter in households demand for cash')\n",
    "    model.param('psid', desc='Ratio of dividends to gross profits')\n",
    "    model.param('psiu', desc='Ratio of retained earnings to investments')\n",
    "    model.param('ro', desc='Reserve requirement parameter')\n",
    "    model.param('sigman', desc='Parameter of influencing normal historic unit costs')\n",
    "    model.param('theta', desc='Income tax rate')\n",
    "    model.param('top', desc='Top value for bank net liquidity ratio')\n",
    "    model.param('xim1', desc='Parameter in the equation for setting interest rate on deposits')\n",
    "    model.param('xim2', 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",
    "\n",
    "    model.param('ADDbl', desc='Spread between long-term interest rate and rate on bills')\n",
    "    model.param('BANDb', desc='Lower range of the flat Phillips curve')\n",
    "    model.param('BANDt', desc='Upper range of the flat Phillips curve')\n",
    "    model.param('GRg', desc='growth_mod of real government expenditures')\n",
    "    model.param('GRpr', desc='growth_mod rate of productivity')\n",
    "    model.param('NCAR', desc='Normal capital adequacy ratio of banks')\n",
    "    model.param('Nfe', desc='Full employment level')\n",
    "    model.param('NPLk', desc='Proportion of Non-Performing loans')\n",
    "    model.param('RA', desc='Random shock to expectations on real sales')\n",
    "    model.param('Rbbar', desc='Interest rate on bills, set exogenously')\n",
    "    model.param('Rln', desc='Normal interest rate on loans')\n",
    "    model.param('sigmas', desc='Realized inventories to sales ratio')\n",
    "    model.param('sigmat', desc='Target inventories to sales ratio')\n",
    "\n",
    "\n",
    "    # Box 11.1 : Firms' equations\n",
    "    # ---------------------------\n",
    "    model.add('Yk = Ske + INke - INk(-1)')          # 11.1 : Real output\n",
    "    model.add('Ske = beta*Sk + (1-beta)*Sk(-1)*(1 + (GRpr + RA))') # 11.2 : Expected real sales\n",
    "    model.add('INke = INk(-1) + gamma*(INkt - INk(-1))')  # 11.3 : Long-run inventory target\n",
    "    model.add('INkt = sigmat*Ske')                  # 11.4 : Short-run inventory target\n",
    "    model.add('INk - INk(-1) = Yk - Sk - NPL/UC')   # 11.5 : Actual real inventories\n",
    "    model.add('Kk = Kk(-1)*(1 + GRk)')              # 11.6 : Real capital stock\n",
    "    model.add('GRk = gamma0 + gammau*U(-1) - gammar*RRl')  # 11.7 : Growth of real capital stock\n",
    "    model.add('U = Yk/Kk(-1)')                      # 11.8 : Capital utilization proxy\n",
    "    model.add('RRl = ((1 + Rl)/(1 + PI)) - 1')      # 11.9 : Real interest rate on loans\n",
    "    model.add('PI = d(P)/P(-1)')                    # 11.10 : Rate of price inflation\n",
    "    model.add('Ik = d(Kk) + delta*Kk(-1)')          # 11.11 : Real gross investment\n",
    "\n",
    "    # Box 11.2 : Firms' equations\n",
    "    # ---------------------------\n",
    "    model.add('Sk = Ck + Gk + Ik')                  # 11.12 : Actual real sales\n",
    "    model.add('S = Sk*P')                           # 11.13 : Value of realized sales\n",
    "    model.add('IN = INk*UC')                        # 11.14 : Inventories valued at current cost\n",
    "    model.add('INV = Ik*P')                         # 11.15 : Nominal gross investment\n",
    "    model.add('K = Kk*P')                           # 11.16 : Nomincal value of fixed capital\n",
    "    model.add('Y = Sk*P + d(INk)*UC')               # 11.17 : Nomincal GDP\n",
    "\n",
    "    # Box 11.3 : Firms' equations\n",
    "    # ---------------------------\n",
    "    # 11.18 : Real wage aspirations\n",
    "    model.add('omegat = exp(omega0 + omega1*log(PR) + omega2*log(ER + z3*(1 - ER) - z4*BANDt + z5*BANDb))')\n",
    "    model.add('ER = N(-1)/Nfe(-1)')                 # 11.19 : Employment rate\n",
    "    # 11.20 : Switch variables\n",
    "    model.add('z3 = if_true(ER > (1-BANDb)) * if_true(ER <= (1+BANDt))')\n",
    "    model.add('z4 = if_true(ER > (1+BANDt))')\n",
    "    model.add('z5 = if_true(ER < (1-BANDb))')\n",
    "    model.add('W - W(-1) = omega3*(omegat*P(-1) - W(-1))')  # 11.21 : Nominal wage\n",
    "    model.add('PR = PR(-1)*(1 + GRpr)')             # 11.22 : Labor productivity\n",
    "    model.add('Nt = Yk/PR')                         # 11.23 : Desired employment\n",
    "    model.add('N - N(-1) = etan*(Nt - N(-1))')      # 11.24 : Actual employment\n",
    "    model.add('WB = N*W')                           # 11.25 : Nominal wage bill\n",
    "    model.add('UC = WB/Yk')                         # 11.26 : Actual unit cost\n",
    "    model.add('NUC = W/PR')                         # 11.27 : Normal unit cost\n",
    "    model.add('NHUC = (1 - sigman)*NUC + sigman*(1 + Rln(-1))*NUC(-1)')  # 11.28 : Normal historic unit cost\n",
    "\n",
    "    # Box 11.4 : Firms' equations\n",
    "    # ---------------------------\n",
    "    model.add('P = (1 + phi)*NHUC')                 # 11.29 : Normal-cost pricing\n",
    "    model.add('phi - phi(-1) = eps2*(phit(-1) - phi(-1))')  # 11.30 : Actual mark-up\n",
    "    # 11.31 : Ideal mark-up\n",
    "    model.add('phit = (FDf + FUft + Rl(-1)*(Lfd(-1) - IN(-1)))/((1 - sigmase)*Ske*UC + (1 + Rl(-1))*sigmase*Ske*UC(-1))')\n",
    "    model.add('HCe = (1 - sigmase)*Ske*UC + (1 + Rl(-1))*sigmase*Ske*UC(-1)')  # 11.32 : Expected historical costs\n",
    "    model.add('sigmase = INk(-1)/Ske')              # 11.33 : Opening inventories to expected sales ratio\n",
    "    model.add('Fft = FUft + FDf + Rl(-1)*(Lfd(-1) - IN(-1))')  # 11.34 : Planned entrepeneurial profits of firmss\n",
    "    model.add('FUft = psiu*INV(-1)')                # 11.35 : Planned retained earnings of firms\n",
    "    model.add('FDf = psid*Ff(-1)')                  # 11.36 : Dividends of firms\n",
    "\n",
    "    # Box 11.5 : Firms' equations\n",
    "    # ---------------------------\n",
    "    model.add('Ff = S - WB + d(IN) - Rl(-1)*IN(-1)')  # 11.37 : Realized entrepeneurial profits\n",
    "    model.add('FUf = Ff - FDf - Rl(-1)*(Lfd(-1) - IN(-1)) + Rl(-1)*NPL')  # 11.38 : Retained earnings of firms\n",
    "    # 11.39 : Demand for loans by firms\n",
    "    model.add('Lfd - Lfd(-1) = INV + d(IN) - FUf - d(Eks)*Pe - NPL')\n",
    "    model.add('NPL = NPLk*Lfs(-1)')                 # 11.40 : Defaulted loans\n",
    "    model.add('Eks - Eks(-1) = ((1 - psiu)*INV(-1))/Pe')  # 11.41 : Supply of equities issued by firms\n",
    "    model.add('Rk = FDf/(Pe(-1)*Ekd(-1))')          # 11.42 : Dividend yield of firms\n",
    "    model.add('PE = Pe/(Ff/Eks(-1))')               # 11.43 : Price earnings ratio\n",
    "    model.add('Q = (Eks*Pe)/(K + IN + Lfd)')        # 11.44 : Tobin's Q ratio\n",
    "\n",
    "    # Box 11.6 : Households' equations\n",
    "    # --------------------------------\n",
    "    model.add('YP = WB + FDf + FDb + Rm(-1)*Md(-1) + Rb(-1)*Bhd(-1) + BLs(-1)')  # 11.45 : Personal income\n",
    "    model.add('T = theta*YP')                       # 11.46 : Income taxes\n",
    "    model.add('YDr = YP - T - Rl(-1)*Lhd(-1)')      # 11.47 : Regular disposable income\n",
    "    model.add('YDhs = YDr + CG')                    # 11.48 : Haig-Simons disposable income\n",
    "    # !1.49 : Capital gains\n",
    "    model.add('CG = d(Pbl)*BLd(-1) + d(Pe)*Ekd(-1) + d(OFb)')\n",
    "    # 11.50 : Wealth\n",
    "    model.add('V - V(-1) = YDr - CONS + d(Pe)*Ekd(-1) + d(Pbl)*BLs(-1) + d(OFb)')\n",
    "    model.add('Vk = V/P')                           # 11.51 : Real staock of wealth\n",
    "    model.add('CONS = Ck*P')                        # 11.52 : Consumption\n",
    "    model.add('Ck = alpha1*(YDkre + NLk) + alpha2*Vk(-1)')  # 11.53 : Real consumption\n",
    "    model.add('YDkre = eps*YDkr + (1 - eps)*YDkr(-1)*(1 + GRpr)')  # 11.54 : Expected real regular disposable income\n",
    "    model.add('YDkr = YDr/P - d(P)*Vk(-1)/P')  # 11.55 : Real regular disposable income\n",
    "\n",
    "    # Box 11.7 : Households' equations\n",
    "    # --------------------------------\n",
    "    model.add('GL = eta*YDr')                       # 11.56 : Gross amount of new personal loans\n",
    "    model.add('eta = eta0 - etar*RRl')              # 11.57 : New loans to personal income ratio\n",
    "    model.add('NL = GL - REP')                      # 11.58 : Net amount of new personal loans\n",
    "    model.add('REP = deltarep*Lhd(-1)')             # 11.59 : Personal loans repayments\n",
    "    model.add('Lhd - Lhd(-1) = GL - REP')           # 11.60 : Demand for personal loans\n",
    "    model.add('NLk = NL/P')                         # 11.61 : Real amount of new personal loans\n",
    "    model.add('BUR = (REP + Rl(-1)*Lhd(-1))/YDr(-1)')  # 11.62 : Burden of personal debt\n",
    "\n",
    "    # Box 11.8 : Households equations - portfolio decisions\n",
    "    # -----------------------------------------------------\n",
    "\n",
    "    # 11.64 : Demand for bills\n",
    "    model.add('Bhd = Vfma(-1)*(lambda20 + lambda22*Rb(-1) - lambda21*Rm(-1) - lambda24*Rk(-1) - lambda23*Rbl(-1) - lambda25*YDr/V)')\n",
    "    # 11.65 : Demand for bonds\n",
    "    model.add('BLd = Vfma(-1)*(lambda30 - lambda32*Rb(-1) - lambda31*Rm(-1) - lambda34*Rk(-1) + lambda33*Rbl(-1) - lambda35*YDr/V)/Pbl')\n",
    "    # 11.66 : Demand for equities - normalized to get the price of equitities\n",
    "    model.add('Pe = Vfma(-1)*(lambda40 - lambda42*Rb(-1) - lambda41*Rm(-1) + lambda44*Rk(-1) - lambda43*Rbl(-1) - lambda45*YDr/V)/Ekd')\n",
    "    model.add('Md = Vfma - Bhd - Pe*Ekd - Pbl*BLd + Lhd')  # 11.67 : Money deposits - as a residual\n",
    "    model.add('Vfma = V - Hhd - OFb')               # 11.68 : Investible wealth\n",
    "    model.add('Hhd = lambdac*CONS')                 # 11.69 : Households' demand for cash\n",
    "    model.add('Ekd = Eks')                          # 11.70 : Stock market equilibrium\n",
    "\n",
    "    # Box 11.9 : Government's equations\n",
    "    # ---------------------------------\n",
    "    model.add('G = Gk*P')                           # 11.71 : Pure government expenditures\n",
    "    model.add('Gk = Gk(-1)*(1 + GRg)')              # 11.72 : Real government expenditures\n",
    "    model.add('PSBR = G + BLs(-1) + Rb(-1)*(Bbs(-1) + Bhs(-1)) - T')  # 11.73 : Government deficit\n",
    "    # 11.74 : New issues of bills\n",
    "    model.add('Bs - Bs(-1) = G - T - d(BLs)*Pbl + Rb(-1)*(Bhs(-1) + Bbs(-1)) + BLs(-1)')\n",
    "    model.add('GD = Bbs + Bhs + BLs*Pbl + Hs')      # 11.75 : Government debt\n",
    "\n",
    "    # Box 11.10 : The Central bank's equations\n",
    "    # ----------------------------------------\n",
    "    model.add('Fcb = Rb(-1)*Bcbd(-1)')              # 11.76 : Central bank profits\n",
    "    model.add('BLs = BLd')                          # 11.77 : Bonds are supplied on demand\n",
    "    model.add('Bhs = Bhd')                          # 11.78 : Household bills supplied on demand\n",
    "    model.add('Hhs = Hhd')                          # 11.79 : Cash supplied on demand\n",
    "    model.add('Hbs = Hbd')                          # 11.80 : Reserves supplied on demand\n",
    "    model.add('Hs = Hbs + Hhs')                     # 11.81 : Total supply of cash\n",
    "    model.add('Bcbd = Hs')                          # 11.82 : Central bankd \n",
    "    model.add('Bcbs = Bcbd')                        # 11.83 : Supply of bills to Central bank\n",
    "    # model.add('Rb = Rbbar')                         # 11.84 : Interest rate on bills set exogenously\n",
    "    model.add('Rbl = Rb + ADDbl')                   # 11.85 : Long term interest rate\n",
    "    model.add('Pbl = 1/Rbl')                        # 11.86 : Price of long-term bonds\n",
    "\n",
    "    # Box 11.11 : Commercial Bank's equations\n",
    "    # ---------------------------------------\n",
    "    model.add('Ms = Md')                            # 11.87 : Bank deposits supplied on demand\n",
    "    model.add('Lfs = Lfd')                          # 11.88 : Loans to firms supplied on demand\n",
    "    model.add('Lhs = Lhd')                          # 11.89 : Personal loans supplied on demand\n",
    "    model.add('Hbd = ro*Ms')                        # 11.90 : Reserve requirements of banks\n",
    "    # 11.91 : Bills supplied to banks\n",
    "    model.add('Bbs - Bbs(-1) = d(Bs) - d(Bhs) - d(Bcbs)')\n",
    "    # 11.92 : Balance sheet constraint of banks\n",
    "    model.add('Bbd = Ms + OFb - Lfs - Lhs - Hbd') \n",
    "    model.add('BLR = Bbd/Ms')                       # 11.93 : Bank liquidity ratio\n",
    "    # 11.94 : Deposit interest rate\n",
    "    model.add('Rm - Rm(-1) = z1a*xim1 + z1b*xim2 - z2a*xim1 - z2b*xim2')\n",
    "    # 11.95-97 : Mechanism for determining changes to the interest rate on deposits\n",
    "    model.add('z2a = if_true(BLR(-1) > (top + .05))')\n",
    "    model.add('z2b = if_true(BLR(-1) > top)')\n",
    "    model.add('z1a = if_true(BLR(-1) <= bot)')\n",
    "    model.add('z1b = if_true(BLR(-1) <= (bot -.05))')\n",
    "\n",
    "    # Box 11.12 : Commercial bank's equations\n",
    "    # ---------------------------------------\n",
    "    model.add('Rl = Rm + ADDl')                     # 11.98 : Loan interest rate\n",
    "    model.add('OFbt = NCAR*(Lfs(-1) + Lhs(-1))')    # 11.99 : Long-run own funds target\n",
    "    model.add('OFbe = OFb(-1) + betab*(OFbt - OFb(-1))')  # 11.100 : Short-run own funds target\n",
    "    model.add('FUbt = OFbe - OFb(-1) + NPLke*Lfs(-1)')  # 11.101 : Target retained earnings of banks\n",
    "    model.add('NPLke = epsb*NPLke(-1) + (1 - epsb)*NPLk(-1)')  # 11.102 : Expected proportion of non-performaing loans\n",
    "    model.add('FDb = Fb - FUb')                     # 11.103 : Dividends of banks\n",
    "    model.add('Fbt = lambdab*Y(-1) + (OFbe - OFb(-1) + NPLke*Lfs(-1))')  # 11.104 : Target profits of banks\n",
    "    # 11.105 : Actual profits of banks\n",
    "    model.add('Fb = Rl(-1)*(Lfs(-1) + Lhs(-1) - NPL) + Rb(-1)*Bbd(-1) - Rm(-1)*Ms(-1)')\n",
    "    # 11.106 : Lending mark-up over deposit rate\n",
    "    model.add('ADDl = (Fbt - Rb(-1)*Bbd(-1) + Rm*(Ms(-1) - (1 - NPLke)*Lfs(-1) - Lhs(-1)))/((1 - NPLke)*Lfs(-1) + Lhs(-1))')\n",
    "    model.add('FUb = Fb - lambdab*Y(-1)')           # 11.107 : Actual retained earnings\n",
    "    model.add('OFb - OFb(-1) = FUb - NPL')          # 11.108 : Own funds of banks\n",
    "    model.add('CAR = OFb/(Lfs + Lhs)')              # 11.109 : Actual capital capacity ratio\n",
    "\n",
    "    model.add('Rb = (1 + RRb)*(1 + PI) - 1')        # 11.111 : Interest rate on bills\n",
    "    model.add('RRbt = (1 + Rb)/(1 + PI) - 1')       # 11.112 : Target real interest rate on bills\n",
    "    model.add('RRb - RRb(-1) = epsrb*(RRbt - RRb(-1))')  # 11.113 : Real interst rate on bills\n",
    "    return model\n",
    "\n",
    "growthb_parameters = {'alpha1': 0.75,\n",
    "                      'alpha2': 0.064,\n",
    "                      'beta': 0.5,\n",
    "                      'betab': 0.4,\n",
    "                      'gamma': 0.15,\n",
    "                      'gamma0': 0.00122,\n",
    "                      'gammar': 0.1,\n",
    "                      'gammau': 0.05,\n",
    "                      'delta': 0.10667,\n",
    "                      'deltarep': 0.1,\n",
    "                      'eps': 0.5,\n",
    "                      'eps2': 0.8,\n",
    "                      'epsb': 0.25,\n",
    "                      'epsrb': 0.9,\n",
    "                      'eta': 0.04918,\n",
    "                      'eta0': 0.07416,\n",
    "                      'etan': 0.6,\n",
    "                      'etar': 0.4,\n",
    "                      'theta': 0.22844,\n",
    "                      'lambda20': 0.25,\n",
    "                      'lambda21': 2.2,\n",
    "                      'lambda22': 6.6,\n",
    "                      'lambda23': 2.2,\n",
    "                      'lambda24': 2.2,\n",
    "                      'lambda25': 0.1,\n",
    "                      'lambda30': -0.04341,\n",
    "                      'lambda31': 2.2,\n",
    "                      'lambda32': 2.2,\n",
    "                      'lambda33': 6.6,\n",
    "                      'lambda34': 2.2,\n",
    "                      'lambda35': 0.1,\n",
    "                      'lambda40': 0.67132,\n",
    "                      'lambda41': 2.2,\n",
    "                      'lambda42': 2.2,\n",
    "                      'lambda43': 2.2,\n",
    "                      'lambda44': 6.6,\n",
    "                      'lambda45': 0.1,\n",
    "                      'lambdab': 0.0153,\n",
    "                      'lambdac': 0.05,\n",
    "                      'xim1': 0.0008,\n",
    "                      'xim2': 0.0007,\n",
    "                      'ro': 0.05,\n",
    "                      'sigman': 0.1666,\n",
    "                      'sigmase': 0.16667,\n",
    "                      'sigmat': 0.2,\n",
    "                      'phi': 0.26417,\n",
    "                      'phit': 0.26417,\n",
    "                      'psid': 0.15255,\n",
    "                      'psiu': 0.92,\n",
    "                      'omega0': -0.20594,\n",
    "                      'omega1': 1,\n",
    "                      'omega2': 2,\n",
    "                      'omega3': 0.45621\n",
    "                      }\n",
    "\n",
    "growthb_exogenous = [('ADDbl', 0.02),\n",
    "                     ('BANDt', 0.01),\n",
    "                     ('BANDb', 0.01),\n",
    "                     ('bot', 0.05),\n",
    "                     ('GRg', 0.03),\n",
    "                     ('GRpr', 0.03),\n",
    "                     ('Nfe', 87.181),\n",
    "                     ('NCAR', 0.1),\n",
    "                     ('NPLk', 0.02),\n",
    "                     ('Rbbar', 0.035),\n",
    "                     ('Rln', 0.07),\n",
    "                     ('RA', 0),\n",
    "                     ('top', 0.12),\n",
    "\n",
    "                     ('ADDl', 0.04592),\n",
    "                     ('BLR', 0.1091),\n",
    "                     ('BUR', 0.06324),\n",
    "                     ('Ck', 7334240),\n",
    "                     ('CAR', 0.09245),\n",
    "                     ('CONS', 52603100),\n",
    "                     ('ER', 1),\n",
    "                     ('Fb', 1744130),\n",
    "                     ('Fbt', 1744140),\n",
    "                     ('Ff', 18081100),\n",
    "                     ('Fft', 18013600),\n",
    "                     ('FDb', 1325090),\n",
    "                     ('FDf', 2670970),\n",
    "                     ('FUb', 419039),\n",
    "                     ('FUf', 15153800),\n",
    "                     ('FUft', 15066200),\n",
    "                     ('G', 16755600),\n",
    "                     ('Gk', 2336160),\n",
    "                     ('GL', 2775900),\n",
    "                     ('GRk', 0.03001),\n",
    "                     ('INV', 16911600),\n",
    "                     ('Ik', 2357910),\n",
    "                     ('N', 'Nfe'),\n",
    "                     ('Nt', 'Nfe'),\n",
    "                     ('NHUC', 5.6735),\n",
    "                     ('NL', 683593),\n",
    "                     ('NLk', 95311),\n",
    "                     ('NPL', 309158),\n",
    "                     ('NPLke', 0.02),\n",
    "                     ('NUC', 5.6106),\n",
    "                     ('omegat', 112852),\n",
    "                     ('P', 7.1723),\n",
    "                     ('Pbl', 18.182),\n",
    "                     ('Pe', 17937),\n",
    "                     ('PE', 5.07185),\n",
    "                     ('PI', 0.0026),\n",
    "                     ('PR', 138659),\n",
    "                     ('PSBR', 1894780),\n",
    "                     ('Q', 0.77443),\n",
    "                     ('Rb', 0.035),\n",
    "                     ('Rbl', 0.055),\n",
    "                     ('Rk', 0.03008),\n",
    "                     ('Rl', 0.06522),\n",
    "                     ('Rm', 0.0193),\n",
    "                     ('REP', 2092310),\n",
    "                     ('RRb', 0.03232),\n",
    "                     ('RRl', 0.06246),\n",
    "                     ('S', 86270300),\n",
    "                     ('Sk', 12028300),\n",
    "                     ('Ske', 'Sk'),\n",
    "                     ('T', 17024100),\n",
    "                     ('U', 0.70073),\n",
    "                     ('UC', 5.6106),\n",
    "                     ('W', 777968),\n",
    "                     ('WB', 67824000),\n",
    "                     ('Y', 86607700),\n",
    "                     ('Yk', 12088400),\n",
    "                     ('YDr', 56446400),\n",
    "                     ('YDkr', 7813270),\n",
    "                     ('YDkre', 7813290),\n",
    "                     ('YP', 73158700),\n",
    "                     ('z1a', 0),\n",
    "                     ('z1b', 0),\n",
    "                     ('z2a', 0),\n",
    "                     ('z2b', 0),\n",
    "                     ]\n",
    "\n",
    "                          \n",
    "growthb_variables = [('Bbd', 4388930),\n",
    "                     ('Bbs', 4389790),\n",
    "                     ('Bcbd', 4655690),\n",
    "                     ('Bcbs', 4655690),\n",
    "                     ('Bhd', 33396900),\n",
    "                     ('Bhs', 'Bhd'),\n",
    "                     ('Bs', 42484800),\n",
    "                     ('BLd', 840742),\n",
    "                     ('BLs', 'BLd'),\n",
    "                     ('GD', 57728700),\n",
    "                     ('Ekd', 5112.6001),\n",
    "                     ('Eks', 'Ekd'),\n",
    "                     ('Hbd', 2025540),\n",
    "                     ('Hbs', 'Hbd'),\n",
    "                     ('Hhd', 2630150),\n",
    "                     ('Hhs', 'Hhd'),\n",
    "                     ('Hs', 'Hbd + Hhd'),\n",
    "                     ('IN', 11585400),\n",
    "                     ('INk', 2064890),\n",
    "                     ('INke', 2405660),\n",
    "                     ('INkt', 'INk'),\n",
    "                     ('K', 127444000),\n",
    "                     ('Kk', 17768900),\n",
    "                     ('Lfd', 15962900),\n",
    "                     ('Lfs', 'Lfd'),\n",
    "                     ('Lhd', 21606600),\n",
    "                     ('Lhs', 'Lhd'),\n",
    "                     ('Md', 40510800),\n",
    "                     ('Ms', 'Md'),\n",
    "                     ('OFb', 3473280),\n",
    "                     ('OFbe', 3782430),\n",
    "                     ('OFbt', 3638100),\n",
    "                     ('V', 165395000),\n",
    "                     ('Vfma', 159291000),\n",
    "                     ('Vk', 22576100),\n",
    "                     ]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scenario: Model GROWTHB, baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "baseline = create_growthb_model()\n",
    "baseline.set_values(growthb_parameters)\n",
    "baseline.set_values(growthb_exogenous)\n",
    "baseline.set_values(growthb_variables)\n",
    "\n",
    "# run to convergence\n",
    "# Give the system more time to reach a steady state\n",
    "for _ in range(100):\n",
    "    baseline.solve(iterations=200, threshold=1e-6)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scenario: Model GROWTHB, increase rate of growth in government expenditure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pysolve.model import SolutionNotFoundError\n",
    "\n",
    "grg = create_growthb_model()\n",
    "grg.set_values(growthb_parameters)\n",
    "grg.set_values(growthb_exogenous)\n",
    "grg.set_values(growthb_variables)\n",
    "\n",
    "# run to convergence\n",
    "# Give the system more time to reach a steady state\n",
    "for _ in range(10):\n",
    "    grg.solve(iterations=200, threshold=1e-6)\n",
    "\n",
    "grg.set_values({'GRg': 0.035})\n",
    "\n",
    "for _ in range(90):\n",
    "    try:\n",
    "        grg.solve(iterations=200, threshold=1e-6)\n",
    "    except SolutionNotFoundError:\n",
    "        grg._update_solutions(grg.solutions[-1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 11.6A"
   ]
  },
  {
   "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 11.6A  Evolution of the growth rate of real output, with the growth rate\n",
    "    of real pure government expenditures being forever higher than in the baseline\n",
    "    solution, when the central bank attempts to keep the real interest rate on bills\n",
    "    at a constant level, but with a partial adjustment function.'''\n",
    "\n",
    "data = list()\n",
    "for i in range(5, 80):\n",
    "    s = grg.solutions[i]\n",
    "    s_1 = grg.solutions[i-1]\n",
    "    data.append((s['Yk']/s_1['Yk']) - 1)\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(0.975, 1.034)\n",
    "\n",
    "axes.plot(data, linestyle='-', color='b')\n",
    "\n",
    "# add labels\n",
    "plt.text(50, 0.028, 'The growth rate')\n",
    "plt.text(50, 0.0275, 'of real output')\n",
    "fig.text(0.1, -.15, caption);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 11.6B"
   ]
  },
  {
   "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 11.6B  Evolution of the nominal bill rate, with the growth rate or real pure\n",
    "    government expenditures being forever higher than in the baseline solution, when\n",
    "    the central bank attempts to keep the real interest rate on bills at a constant level,\n",
    "    but with a partial adjustment solution.'''\n",
    "\n",
    "data = [s['Rb'] for s in grg.solutions[5:80]]\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(0.975, 1.034)\n",
    "\n",
    "axes.plot(data, linestyle='-', color='b')\n",
    "\n",
    "# add labels\n",
    "plt.text(27, 0.085, 'Nominal bill rate')\n",
    "fig.text(0.1, -.15, 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
}