Chapter 12 Model OPENFIXG.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Monetary Econonmics: Chapter 12"
   ]
  },
  {
   "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 OPENFIXG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_openfixg_model():\n",
    "    model = Model()\n",
    "        \n",
    "    model.set_var_default(0)\n",
    "    model.set_param_default(0)\n",
    "    model.var('BdUKUK', desc='Bills issued by the UK acquired by the UK: demand')\n",
    "    model.var('BsUKUK', desc='Bills issued by the UK acquired by the UK: supply')\n",
    "    model.var('BcbdUKUS', desc='Bills issued by the US, demanded by the UK central bank')\n",
    "    model.var('BcbdUKUK', desc='Bills issued by the UK, demanded by the UK central bank')\n",
    "    model.var('BcbsUKUS', desc='Bills issued by the US, supplied to the UK central bank')\n",
    "    model.var('BsUK', desc='Bills issued by the UK - total supply')\n",
    "    model.var('BdUKUS', desc='Bills issued by the US acquired by the UK: demand')\n",
    "    model.var('BsUKUS', desc='Bills issued by the US acquired by the UK: supply')\n",
    "    model.var('BdUSUK', desc='Bills issued by the UK acquired by the US: demand')\n",
    "    model.var('BsUSUK', desc='Bills issued by the UK acquired by the US: supply')\n",
    "    model.var('BsUS', desc='Bills issued by the US - total supply')\n",
    "    model.var('BdUSUS', desc='Bills issued by the US acquired by the US: demand')\n",
    "    model.var('BsUSUS', desc='Bills issued by the US acquired by the US: supply')\n",
    "    model.var('BcbdUSUS', desc='Bills issued by the US, demanded by the US central bank')\n",
    "    model.var('BcbsUSUS', desc='Bills issued by the US, supplied to the US central bank')\n",
    "    model.var('CkUK', desc='Real consumption in the UK')\n",
    "    model.var('CkUS', desc='Real consumption in the US')\n",
    "    model.var('CABUK', desc='Current account balance in the UK')\n",
    "    model.var('CABUS', desc='Current account balance in the US')\n",
    "    model.var('CONSUK', desc='Consumption in the UK')\n",
    "    model.var('CONSUS', desc='Consumption in the US')\n",
    "    model.var('DSUK', desc='Domestic sales in the UK')\n",
    "    model.var('DSUS', desc='Domestic sales in the US')\n",
    "    model.var('DSkUK', desc='Real domestic sales in the UK')\n",
    "    model.var('DSkUS', desc='Real domestic sales in the US')\n",
    "    model.var('FcbUK', desc='Profits of the central bank in the UK')\n",
    "    model.var('FcbUS', desc='Profits of the central bank in the US')\n",
    "    model.var('GkUK', desc='Real government expenditure in the UK')\n",
    "    model.var('GUK', desc='Government expenditure in the UK')\n",
    "    model.var('GUS', desc='Government expenditure in the US')\n",
    "    model.var('HdUK', desc='Demand for cash of the UK')\n",
    "    model.var('HsUK', desc='Supply of cash for the UK')\n",
    "    model.var('HdUS', desc='Demand for cash of the US')\n",
    "    model.var('HsUS', desc='Supply for cash for the US')\n",
    "    model.var('IMUK', desc='Imports of the UK from the US')\n",
    "    model.var('IMUS', desc='Imports of the US from the UK')\n",
    "    model.var('IMkUK', desc='Real imports of the UK from the US')\n",
    "    model.var('IMkUS', desc='Real imports of the US from the UK')\n",
    "    model.var('KABUK', desc='Current account balance in the UK')\n",
    "    model.var('KABUS', desc='Current account balance in the US')\n",
    "    model.var('KABPUK', desc='Capital account balance in the UK, excluding official transactions')\n",
    "    model.var('KABPUS', desc='Capital account balance in the US, excluding official transactions')\n",
    "    model.var('NAFAUK', desc='Net accumulation of financial assets in the UK')\n",
    "    model.var('NAFAUS', desc='Net accumulation of financial assets in the US')\n",
    "    model.var('NUK', desc='Employment in the UK')\n",
    "    model.var('NUS', desc='Employment in the US')\n",
    "    model.var('PDSUK', desc='Price of domestic sales in the UK')\n",
    "    model.var('PDSUS', desc='Price of domestic sales in the US')\n",
    "    model.var('PGUK', desc='Price of gold in the UK')\n",
    "    model.var('PMUK', desc='Price of imports in the UK')\n",
    "    model.var('PMUS', desc='Price of imports in the US')\n",
    "    model.var('PSUK', desc='Price of sales in the UK')\n",
    "    model.var('PSUS', desc='Price of sales in the US')\n",
    "    model.var('PSBRUK', desc='Government deficit in the UK')\n",
    "    model.var('PSBRUS', desc='Government deficit in the US')\n",
    "    model.var('PYUK', desc='Price of output in the UK')\n",
    "    model.var('PYUS', desc='Price of output in the US')\n",
    "    model.var('PXUK', desc='Price of exports in the UK')\n",
    "    model.var('PXUS', desc='Price of exports in the US')\n",
    "    model.var('SUK', desc='Real sales in the UK')\n",
    "    model.var('SUS', desc='Real sales in the US')\n",
    "    model.var('SkUK', desc='Real sales in the UK')\n",
    "    model.var('SkUS', desc='Real sales in the US')\n",
    "    model.var('TUK', desc='Tax revenue in the UK')\n",
    "    model.var('TUS', desc='Tax revenue in the US')\n",
    "    model.var('VUK', desc='Net financial assets of the UK')\n",
    "    model.var('VUS', desc='Net financial assets of the US')\n",
    "    model.var('VkUK', desc='Real net financial assets of the UK')\n",
    "    model.var('VkUS', desc='Real net financial assets of the US')\n",
    "    model.var('XUK', desc='Exports from the UK to the US')\n",
    "    model.var('XUS', desc='Exports from the US to the UK')\n",
    "    model.var('XkUK', desc='Real exports from the U to the UK')\n",
    "    model.var('XkUS', desc='Real exports from the U to the US')\n",
    "    model.var('XRUS', desc='Exchange rate: units of US currency against 1 unit of UK currency')\n",
    "    model.var('YDrUK', desc='Disposable income in the UK')\n",
    "    model.var('YDrUS', desc='Disposable income in the US')\n",
    "    model.var('YDhsUK', desc='Haig-Simons disposable income in the UK')\n",
    "    model.var('YDhsUS', desc='Haig-Simons disposable income in the US')\n",
    "    model.var('YDhskUK', desc='Real Haig-Simons disposable income in the UK')\n",
    "    model.var('YDhskUS', desc='Real Haig-Simons disposable income in the US')\n",
    "    model.var('YDhsekUK', desc='Expected real Haig-Simons disposable income in the UK')\n",
    "    model.var('YDhsekUS', desc='Expected real Haig-Simons disposable income in the US')\n",
    "    model.var('YUK', desc='Income in the UK')\n",
    "    model.var('YUS', desc='Income in the US')\n",
    "    model.var('YkUK', desc='Real income in the UK')\n",
    "    model.var('YkUS', desc='Real income in the US')\n",
    "\n",
    "    model.param('BcbsUKUK', desc='Bills issued by the UK, supplied to the UK central bank')\n",
    "    model.param('DXREUK', desc='Expected change in the exchange rate of the UK (measured as units of the UK currency against 1 unit of the US currency)')\n",
    "    model.param('DXREUS', desc='Expected change in the exchange rate of the US (measured as units of the US currency against 1 unit of the UK currency)')\n",
    "    model.param('GkUS', desc='Real government expenditure in the US')\n",
    "    model.param('ORUK', desc='Gold reserves in the UK')\n",
    "    model.param('ORUS', desc='Gold reserves in the US')\n",
    "    model.param('PGUS', desc='Price of gold in the US')\n",
    "    model.param('PRUK', desc='Productivity in the UK')\n",
    "    model.param('PRUS', desc='Productivity in the US')\n",
    "    model.param('RUK', desc='Interest rate on the UK bills')\n",
    "    model.param('RUS', desc='Interest rate on the US bills')\n",
    "    model.param('WUK', desc='Nominal wage rate in the UK')\n",
    "    model.param('WUS', desc='Nominal wage rate in the US')\n",
    "    model.param('XREUK', desc='Expected exchange rate: units of UK currency against 1 unit of US currency')\n",
    "    model.param('XREUS', desc='Expected exchange rate: units of US currency against 1 unit of UK currency')\n",
    "    model.param('XRUK', desc='Exchange rate: units of UK currency against 1 unit of US currency')\n",
    "\n",
    "\n",
    "    model.param('alpha1UK', desc='Propensity to consume out of income in the UK')\n",
    "    model.param('alpha2UK', desc='Propensity to consume out of wealth in the UK')\n",
    "    model.param('alpha1US', desc='Propensity to consume out of income in the US')\n",
    "    model.param('alpha2US', desc='Propensity to consume out of wealth in the US')\n",
    "    model.param('eps0', desc='Parameter determining real exports in the UK')\n",
    "    model.param('eps1', desc='Parameter determining real exports in the UK')\n",
    "    model.param('eps2', desc='Parameter determining real exports in the UK')\n",
    "    model.param('lambda10', desc='Parameter in asset demand function')\n",
    "    model.param('lambda11', desc='Parameter in asset demand function')\n",
    "    model.param('lambda12', desc='Parameter in asset demand function')\n",
    "    model.param('lambda20', desc='Parameter in asset demand function')\n",
    "    model.param('lambda21', desc='Parameter in asset demand function')\n",
    "    model.param('lambda22', desc='Parameter in asset demand function')\n",
    "    model.param('lambda30', desc='Parameter in asset demand function')\n",
    "    model.param('lambda31', desc='Parameter in asset demand function')\n",
    "    model.param('lambda32', desc='Parameter in asset demand function')\n",
    "    model.param('lambda40', desc='Parameter in asset demand function')\n",
    "    model.param('lambda41', desc='Parameter in asset demand function')\n",
    "    model.param('lambda42', desc='Parameter in asset demand function')\n",
    "    model.param('lambda50', desc='Parameter in asset demand function')\n",
    "    model.param('lambda51', desc='Parameter in asset demand function')\n",
    "    model.param('lambda52', desc='Parameter in asset demand function')\n",
    "    model.param('mu0', desc='Parameter determining real imports in the UK')\n",
    "    model.param('mu1', desc='Parameter determining real imports in the UK')\n",
    "    model.param('mu2', desc='Parameter determining real imports in the UK')\n",
    "    model.param('nu0m', desc='Parameter determining import prices in the UK')\n",
    "    model.param('nu1m', desc='Parameter determining import prices in the UK')\n",
    "    model.param('nu0x', desc='Parameter determining import prices in the UK')\n",
    "    model.param('nu1x', desc='Parameter determining import prices in the UK')\n",
    "    model.param('thetaUK', desc='Tax rate in the UK')\n",
    "    model.param('thetaUS', desc='Tax rate in the US')\n",
    "    model.param('phiUK', desc='mark-up in the UK')\n",
    "    model.param('phiUS', desc='mark-up in the US')\n",
    "\n",
    "\n",
    "    # Accounting Identities\n",
    "    # ---------------------\n",
    "    # 12.1 : Disposable income in the UK\n",
    "    model.add('YDrUK = (YUK + RUK(-1)*BdUKUK(-1) + XRUS*RUS(-1)*BsUKUS(-1))*(1 - thetaUK) + d(XRUS)*BsUKUS(-1)')\n",
    "    model.add('YDhsUK = YDrUK + d(XRUS)*BsUKUS(-1)') # 12.2 : Haig-Simons disposable income in the UK\n",
    "    model.add('VUK - VUK(-1) = YDrUK - CONSUK')      # 12.3 : Wealth accumulation in the UK\n",
    "    # 12.4 : Disposable income in the US\n",
    "    model.add('YDrUS = (YUS + RUS(-1)*BdUSUS(-1) + XRUK*RUK(-1)*BsUSUK(-1))*(1 - thetaUS) + d(XRUK)*BsUSUK(-1)')\n",
    "    model.add('YDhsUS = YDrUS + d(XRUK)*BsUSUK(-1)') # 12.5 : Haig-Simons disposable income in the US\n",
    "    model.add('VUS - VUS(-1) = YDrUS - CONSUS')      # 12.6 : Wealth accumulation in the US\n",
    "    model.add('TUK = thetaUK*(YUK + RUK(-1)*BdUKUK(-1) + XRUS*RUS(-1)*BsUKUS(-1))')  # 12.7 : Taxes in the UK\n",
    "    model.add('TUS = thetaUS*(YUS + RUS(-1)*BdUSUS(-1) + XRUK*RUK(-1)*BsUSUK(-1))')  # 12.8 : Taxes in the US\n",
    "\n",
    "    # Equations 12.9 and 12.10 dropped in favor on 12.53 and 12.54\n",
    "\n",
    "    model.add('FcbUK = RUK(-1)*BcbdUKUK(-1) + RUS(-1)*BcbsUKUS(-1)*XRUS')  # 12.11 : UK central bank profits\n",
    "    model.add('FcbUS = RUS(-1)*BcbdUSUS(-1)')        # 12.12 : US central bank profits\n",
    "    # 12.13G : Government budget constraint, now solved for GUK\n",
    "    model.add('GUK = BsUK - BsUK(-1) - (RUK(-1)*BsUK(-1) - TUK - FcbUK)')\n",
    "    # model.add('BsUK = BsUK(-1) + GUK + RUK(-1)*BsUK(-1) - TUK - FcbUK')  # 12.13 : UK Govt budget constraint\n",
    "    model.add('BsUS = BsUS(-1) + GUS + RUS(-1)*BsUS(-1) - TUS - FcbUS')  # 12.14 : US Govt budget constraint\n",
    "    # 12.15 : UK Current account balance\n",
    "    model.add('CABUK = XUK - IMUK + XRUS*RUS(-1)*BsUKUS(-1) - RUK(-1)*BsUSUK(-1) + RUS(-1)*BcbsUKUS(-1)*XRUS')\n",
    "    # 12.16 : UK Capital account balance\n",
    "    model.add('KABUK = KABPUK - (XRUS*d(BcbsUKUS) + PGUK*d(ORUK))')\n",
    "    # 12.17 : US Current acount balance\n",
    "    model.add('CABUS = XUS - IMUS + XRUK*RUK(-1)*BsUSUK(-1) - RUS(-1)*BsUKUS(-1) - RUS(-1)*BcbsUKUS(-1)')\n",
    "    # 12.18 : US Capital account balance\n",
    "    model.add('KABUS = KABPUS + d(BcbsUKUS) - PGUS*d(ORUS)')\n",
    "    model.add('KABPUK = -d(BsUKUS)*XRUS + d(BsUSUK)')   # 12.19 : UK capital account balance, net of official transactions\n",
    "    model.add('KABPUS = -d(BsUSUK)*XRUK + d(BsUKUS)')   # 12.20 : US capital account balance, net of official transactions\n",
    "\n",
    "    # Trade\n",
    "    # -----\n",
    "    # 12.21 : Import prices in UK\n",
    "    model.add('PMUK = exp(nu0m + nu1m*log(PYUS) + (1 - nu1m)*log(PYUK) - nu1m*log(XRUK))')\n",
    "    # 12.22 : Export prices in UK\n",
    "    model.add('PXUK = exp(nu0x + nu1x*log(PYUS) + (1 - nu1x)*log(PYUK) - nu1x*log(XRUK))')\n",
    "    model.add('PXUS = PMUK*XRUK')               # 12.23 : Export prices in US\n",
    "    model.add('PMUS = PXUK*XRUK')               # 12.24 : Import prices in US\n",
    "    # 12.25 : Real exports from UK, depends on current relative price    \n",
    "    model.add('XkUK = exp(eps0 - eps1*log(PMUS/PYUS) + eps2*log(YkUS))')\n",
    "    # 12.26 : Real imports of UK\n",
    "    model.add('IMkUK = exp(mu0 - mu1*log(PMUK(-1)/PYUK(-1)) + mu2*log(YkUK))')\n",
    "    model.add('XkUS = IMkUK')                   # 12.27 : Real exports from US\n",
    "    model.add('IMkUS = XkUK')                   # 12.28 : Real imports of US\n",
    "    model.add('XUK = XkUK*PXUK')                # 12.29 : Exports of UK\n",
    "    model.add('XUS = XkUS*PXUS')                # 12.30 : Exports of US\n",
    "    model.add('IMUK = IMkUK*PMUK')              # 12.31 : Imports of UK\n",
    "    model.add('IMUS = IMkUS*PMUS')              # 12.32 : Imports of US\n",
    "\n",
    "    # Income and expenditure\n",
    "    # ----------------------\n",
    "    model.add('VkUK = VUK/PDSUK')               # 12.33 : Real wealth in UK\n",
    "    model.add('VkUS = VUS/PDSUS')               # 12.34 : Real wealth in US\n",
    "    # 12.35 : Real Haig-Simons disposable income in UK\n",
    "    model.add('YDhskUK = YDrUK/PDSUK - VkUK(-1)*d(PDSUK)/PDSUK')\n",
    "    # 12.36 : Real Haig-Simons disposable income in US\n",
    "    model.add('YDhskUS = YDrUS/PDSUS - VkUS(-1)*d(PDSUS)/PDSUS') \n",
    "    # 12.37 : Real consumption in UK\n",
    "    model.add('CkUK = alpha1UK*YDhsekUK + alpha2UK*VkUK(-1)')\n",
    "    # 12.38 : Real consumption in US\n",
    "    model.add('CkUS = alpha1US*YDhsekUS + alpha2US*VkUS(-1)')\n",
    "    # 12.39  Expected real Haig-Simons disposable income in UK\n",
    "    model.add('YDhsekUK = (YDhskUK + YDhskUK(-1))/2')\n",
    "    # 12.40  Expected real Haig-Simons disposable income in US\n",
    "    model.add('YDhsekUS = (YDhskUS + YDhskUS(-1))/2')\n",
    "    model.add('SkUK = CkUK + GkUK + XkUK')      # 12.41 : Real sales in UK\n",
    "    model.add('SkUS = CkUS + GkUS + XkUS')      # 12.42 : Real sales in US\n",
    "    model.add('SUK = SkUK*PSUK')                # 12.43 : Value of sales in UK\n",
    "    model.add('SUS = SkUS*PSUS')                # 12.44 : Value of sales in US\n",
    "    model.add('PSUK = (1 + phiUK)*(WUK*NUK + IMUK)/SkUK') # 12.45 : Price of sales in UK\n",
    "    model.add('PSUS = (1 + phiUS)*(WUS*NUS + IMUS)/SkUS') # 12.46 : Price of sales in US\n",
    "    model.add('PDSUK = (SUK - XUK)/(SkUK - XkUK)') # 12.47 : Price of domestic sales in UK\n",
    "    model.add('PDSUS = (SUS - XUS)/(SkUS - XkUS)') # 12.48 : Price of domestic sales in US\n",
    "    model.add('DSUK = SUK - XUK')               # 12.49 : Domestic sales in UK\n",
    "    model.add('DSUS = SUS - XUS')               # 12.50 : Domestic sales in US\n",
    "    model.add('DSkUK = CkUK + GkUK')            # 12.51 : Real domestic sales in UK\n",
    "    model.add('DSkUS = CkUS + GkUS')            # 12.52 : Real domestic sales in US\n",
    "    model.add('YUK = SUK - IMUK')               # 12.53 : Value of output in UK\n",
    "    model.add('YUS = SUS - IMUS')               # 12.54 : Value of output in US\n",
    "    model.add('YkUK = SkUK - IMkUK')            # 12.55 : Value of real output in UK\n",
    "    model.add('YkUS = SkUS - IMkUS')            # 12.56 : Value of real output in US\n",
    "    model.add('PYUK = YUK/YkUK')                # 12.57 : Price of output in UK\n",
    "    model.add('PYUS = YUS/YkUS')                # 12.58 : Price of output in US\n",
    "    model.add('CONSUK = CkUK*PDSUK')            # 12.59 : Consumption in UK\n",
    "    model.add('CONSUS = CkUS*PDSUS')            # 12.60 : Consumption in US\n",
    "    # model.add('GUK = GkUK*PDSUK')               # 12.61 : Govt expenditure in UK\n",
    "    # 12.61G : Govt expenditure in UK, now solved for GkUK\n",
    "    model.add('GkUK = GUK/PDSUK')\n",
    "    model.add('GUS = GkUS*PDSUS')               # 12.62 : Govt expenditure in US\n",
    "\n",
    "    # Note : tax definitions in the book as eqns 12.63 and 12.64 are\n",
    "    # already defined here as eqns 12.7 and 12.8\n",
    "\n",
    "    model.add('NUK = YkUK/PRUK')                # 12.65 : Employment in UK\n",
    "    model.add('NUS = YkUS/PRUS')                # 12.66 : Employment in US\n",
    "\n",
    "    # Asset Demands\n",
    "    # -------------\n",
    "    # 12.67 : Demand for UK bills in UK\n",
    "    model.add('BdUKUK = VUK*(lambda10 + lambda11*RUK - lambda12*(RUS + DXREUS))')\n",
    "    # 12.68 : Demand for US bills in UK\n",
    "    model.add('BdUKUS = VUK*(lambda20 - lambda21*RUK + lambda22*(RUS + DXREUS))')\n",
    "    model.add('HdUK = VUK - BdUKUK - BdUKUS')   # 12.69 : Demand for money in UK\n",
    "    # 12.70 : Demand for US bills in US\n",
    "    model.add('BdUSUS = VUS*(lambda40 + lambda41*RUS - lambda42*(RUK + DXREUK))')\n",
    "    # 12.71 : Demand for UK bills in US\n",
    "    model.add('BdUSUK = VUS*(lambda50 - lambda51*RUS + lambda52*(RUK + DXREUK))')\n",
    "    model.add('HdUS = VUS - BdUSUS - BdUSUK')   # 12.72 : Demand for money in US\n",
    "\n",
    "    # Asset Supplies\n",
    "    # --------------\n",
    "    model.add('HsUS = HdUS')                    # 12.77 : Supply of cash in US\n",
    "    model.add('BsUSUS = BdUSUS')                # 12.78 : Supply of US bills to US\n",
    "    model.add('BcbsUSUS = BcbdUSUS')            # 12.79 : Supply of US bills to US central bank\n",
    "    model.add('HsUK = HdUK')                    # 12.80 : Supply of cash in UK\n",
    "    model.add('BsUKUK = BdUKUK')                # 12.81 : Bills issued by UK acquired by UK\n",
    "    # model.add('BcbsUKUK = BcbdUKUK')            # 12.82 : Supply of UK bills to UK central bank\n",
    "    # 12.82G : Supply of UK bills to UK central bank\n",
    "    model.add('BsUK = BsUKUK + BsUSUK + BcbsUKUK')\n",
    "    # 12.83 : Balance sheet of US central bank\n",
    "    model.add('BcbdUSUS = BcbdUSUS(-1) + d(HsUS) - d(ORUS)*PGUS ')\n",
    "    # 12.84 : Balance sheet of UK central bank\n",
    "    model.add('BcbdUKUK = BcbdUKUK(-1) + d(HsUK) - d(BcbsUKUS)*XRUS - d(ORUK)*PGUK')\n",
    "    model.add('PGUK = PGUS/XRUK')               # 12.85 : Price of gold is equal in US and UK\n",
    "    model.add('XRUS = 1/XRUK')                  # 12.86 : US exchange rate\n",
    "    model.add('BsUSUK = BdUSUK*XRUS')           # 12.87 : Equilibrium condition for bills issued by UK acquired by US\n",
    "    model.add('BcbdUKUS = BcbsUKUS*XRUS')       # 12.88 : Equilibrium conditioin for bills issued by US acquired by UK central bank\n",
    "    # XRUK is exogenous\n",
    "    # model.add('XRUK = BsUKUS/BdUKUS')           # 12.89FL : \n",
    "    # 12.89G : Bills supply from UK to US\n",
    "    model.add('BsUKUS = XRUK*BdUKUS')\n",
    "    # 12.90GA : Supply of UK bills to US, now solved for BcbsUKUS\n",
    "    model.add('BcbsUKUS = BsUS - BsUSUS - BcbdUSUS - BsUKUS')\n",
    "    # Government deficits in the UK\n",
    "    model.add('PSBRUK = GUK + RUK(-1)*BsUK(-1) - TUK - FcbUK')\n",
    "    # Government deficits in the US\n",
    "    model.add('PSBRUS = GUS + RUS(-1)*BsUS(-1) - TUS - FcbUS')\n",
    "    model.add('NAFAUK = PSBRUK + CABUK')        # Net accumulation of financial assets in the UK\n",
    "    model.add('NAFAUS = PSBRUS + CABUS')        # Net accumulation of financial assets in the US\n",
    "\n",
    "    return model\n",
    "\n",
    "openfixg_parameters = {'alpha1UK': 0.75,\n",
    "                      'alpha1US': 0.75,\n",
    "                      'alpha2UK': 0.13333,\n",
    "                      'alpha2US': 0.13333,\n",
    "                      'eps0': -2.1,\n",
    "                      'eps1': 0.7,\n",
    "                      'eps2': 1,\n",
    "                      'lambda10': 0.7,\n",
    "                      'lambda11': 5,\n",
    "                      'lambda12': 5,\n",
    "                      'lambda20': 0.25,\n",
    "                      'lambda21': 5,\n",
    "                      'lambda22': 5,\n",
    "                      'lambda40': 0.7,\n",
    "                      'lambda41': 5,\n",
    "                      'lambda42': 5,\n",
    "                      'lambda50': 0.25,\n",
    "                      'lambda51': 5,\n",
    "                      'lambda52': 5,\n",
    "                      'mu0': -2.1,\n",
    "                      'mu1': 0.7,\n",
    "                      'mu2': 1,\n",
    "                      'nu0m': -0.00001,\n",
    "                      'nu0x': -0.00001,\n",
    "                      'nu1m': 0.7,\n",
    "                      'nu1x': 0.5,\n",
    "                      'phiUK': 0.2381,\n",
    "                      'phiUS': 0.2381,\n",
    "                      'thetaUK': 0.2,\n",
    "                      'thetaUS': 0.2,\n",
    "                     }\n",
    "\n",
    "openfixg_exogenous = {'BcbsUKUS': 0.02031,\n",
    "                     'DXREUS': 0,\n",
    "                     'GkUK': 16,\n",
    "                     'GkUS': 16,\n",
    "                     'ORUK': 7,\n",
    "                     'PGUS': 1,\n",
    "                     'PRUK': 1.3333,\n",
    "                     'PRUS': 1.3333,\n",
    "                     'RUK': 0.03,\n",
    "                     'RUS': 0.03,\n",
    "                     'WUK': 1,\n",
    "                     'WUS': 1,\n",
    "                     'BcbdUKUK': 0.27984,\n",
    "                     'BcbsUKUK': 0.27984,\n",
    "                     'BcbdUKUS': 0.0203,\n",
    "                     'BcbdUSUS': 0.29843,\n",
    "                     'BcbsUSUS': 0.29843,\n",
    "                     'BsUK': 138.94,\n",
    "                     'BdUKUK': 102.18,\n",
    "                     'BsUKUK': 102.18,\n",
    "                     'BdUKUS': 36.493,\n",
    "                     'BsUKUS': 36.504,\n",
    "                     'BsUS': 139.02,\n",
    "                     'BdUSUK': 36.497,\n",
    "                     'BsUSUK': 36.487,\n",
    "                     'BdUSUS': 102.19,\n",
    "                     'BsUSUS': 102.19,\n",
    "                     'HdUK': 7.2987,\n",
    "                     'HsUK': 7.2987,\n",
    "                     'HdUS': 7.2995,\n",
    "                     'HsUS': 7.2995,\n",
    "                     'ORUS': 7,\n",
    "                     'VkUK': 152.62,\n",
    "                     'VkUS': 152.63,\n",
    "                     'VUK': 145.97,\n",
    "                     'VUS': 145.99001,\n",
    "                     'CkUK': 81.393,\n",
    "                     'CkUS': 81.401,\n",
    "                     'CABUK': 0,\n",
    "                     'CABUS': 0,\n",
    "                     'CONSUK': 77.851,\n",
    "                     'CONSUS': 77.86,\n",
    "                     'DSkUK': 97.393,\n",
    "                     'DSkUS': 97.401,\n",
    "                     'DSUK': 93.154,\n",
    "                     'DSUS': 93.164,\n",
    "                     'DXREUK': 0,\n",
    "                     'FcbUK': 0.00869,\n",
    "                     'FcbUS': 0.00895,\n",
    "                     'GUK': 15.304,\n",
    "                     'GUS': 15.304,\n",
    "                     'IMkUK': 11.928,\n",
    "                     'IMkUS': 11.926,\n",
    "                     'IMUK': 11.407,\n",
    "                     'IMUS': 11.409,\n",
    "                     'KABPUK': 0.00002,\n",
    "                     'KABPUS': -0.00002,\n",
    "                     'NUK': 73.046,\n",
    "                     'NUS': 73.054,\n",
    "                     'PDSUK': 0.95648,\n",
    "                     'PDSUS': 0.95649,\n",
    "                     'PGUK': 0.99971,\n",
    "                     'PMUK': 0.95628,\n",
    "                     'PMUS': 0.95661,\n",
    "                     'PSUK': 0.95646,\n",
    "                     'PSUS': .9565,\n",
    "                     'PXUK': 0.95634,\n",
    "                     'PXUS': 0.95656,\n",
    "                     'PYUK': 0.95648,\n",
    "                     'PYUS': 0.95649,\n",
    "                     'SkUK': 109.32,\n",
    "                     'SkUS': 109.33,\n",
    "                     'SUK': 104.56,\n",
    "                     'SUS': 104.57,\n",
    "                     'TUK': 19.463,\n",
    "                     'TUS': 19.465,\n",
    "                     'XkUK': 11.926,\n",
    "                     'XkUS': 11.928,\n",
    "                     'XUK': 11.406,\n",
    "                     'XUS': 11.41,\n",
    "                     'XRUK': 1.0003,\n",
    "                     'XRUS': 0.99971,\n",
    "                     'XREUK': 1.0003,\n",
    "                     'XREUS': 0.99971,\n",
    "                     'YkUK': 97.392,\n",
    "                     'YkUS': 97.403,\n",
    "                     'YUK': 93.154,\n",
    "                     'YUS': 93.164,\n",
    "                     'YDrUK': 77.851,\n",
    "                     'YDrUS': 77.86,\n",
    "                     'YDhskUK': 81.394,\n",
    "                     'YDhskUS': 81.402,\n",
    "                     'YDhsekUK': 81.394,\n",
    "                     'YDhsekUS': 81.402,\n",
    "                    }\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scenario: Model OPENFIXG, baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "baseline = create_openfixg_model()\n",
    "baseline.set_values(openfixg_parameters)\n",
    "baseline.set_values(openfixg_exogenous)\n",
    "\n",
    "# To get the model to converge, I use a different method for solving the set of equations.\n",
    "for i in range(100):\n",
    "    baseline.solve(iterations=200, threshold=1e-4, method='broyden')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scenario: Model OPENFIXG, increase the UK propensity to import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu0 = create_openfixg_model()\n",
    "mu0.set_values(openfixg_parameters)\n",
    "mu0.set_values(openfixg_exogenous)\n",
    "\n",
    "for _ in range(10):\n",
    "    mu0.solve(iterations=200, threshold=1e-4, method='broyden')\n",
    "\n",
    "mu0.set_values({'mu0': -2.0})\n",
    "\n",
    "for _ in range(90):\n",
    "    mu0.solve(iterations=200, threshold=1e-4, method='broyden')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 12.3A"
   ]
  },
  {
   "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 12.3A  Effect of an increase in the UK propensity to import within a fixed\n",
    "    exchange rate regime with endogenous UK government expenditures, on the US and\n",
    "    UK real GDP.'''\n",
    "usdata = [s['YkUS'] for s in mu0.solutions[5:50]]\n",
    "ukdata = [s['YkUK'] for s in mu0.solutions[5:50]]\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(usdata, linestyle='-', color='b')\n",
    "axes.plot(ukdata, linestyle='--', linewidth=2, color='g')\n",
    "\n",
    "# add labels\n",
    "plt.text(15, 97, 'US real GDP')\n",
    "plt.text(15, 90, 'UK real GDP')\n",
    "fig.text(0.1, -.1, caption);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 12.3B"
   ]
  },
  {
   "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 12.3B  Effect of an increase in the UK propensity to import within a fixed\n",
    "    exchange rate regime with endogenous UK interest rates, on various UK variables:\n",
    "    the capital account balance, the current account balance and the trade balance.'''\n",
    "cabdata = [s['CABUK'] for s in mu0.solutions[5:50]]\n",
    "xidata = [s['XUK'] - s['IMUK'] for s in mu0.solutions[5:50]]\n",
    "kabpdata = [s['KABPUK'] for s in mu0.solutions[5:50]]\n",
    "\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(cabdata, linestyle='-', color='b')\n",
    "axes.plot(xidata, linestyle='--', linewidth=2, color='g')\n",
    "axes.plot(kabpdata, linestyle=':', linewidth=2, color='r')\n",
    "\n",
    "# add labels\n",
    "plt.text(25, -0.15, 'UK current account balance')\n",
    "plt.text(30, 0.15, 'UK trade balance')\n",
    "plt.text(9, 0.3, 'UK capital account balance')\n",
    "fig.text(0.1, -.1, caption);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 12.3C"
   ]
  },
  {
   "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 12.3C  Effect of an increase in the UK propensity to import within a fixed\n",
    "    exchange rate regime with endogenous UK interest rates, on various UK variables:\n",
    "    the capital account balance, the government budget balance, and the net accumulation\n",
    "    of financial assets.'''\n",
    "nafadata = [s['NAFAUK'] for s in mu0.solutions[5:50]]\n",
    "cabdata = [s['CABUK'] for s in mu0.solutions[5:50]]\n",
    "psbrdata = [-s['PSBRUK'] for s in mu0.solutions[5:50]]\n",
    "\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(nafadata, linestyle='-', color='b')\n",
    "axes.plot(cabdata, linestyle='--', linewidth=2, color='g')\n",
    "axes.plot(psbrdata, linestyle=':', linewidth=2, color='r')\n",
    "\n",
    "# add labels\n",
    "plt.text(10, -1, 'UK net accumulation')\n",
    "plt.text(10, -1.15, 'of financial assets')\n",
    "plt.text(7, -0.05, 'UK current account balance')\n",
    "plt.text(10, 0.65, 'UK government budget balance')\n",
    "fig.text(0.1, -.15, caption);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 12.3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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 12.3D  Effect of an increase in the UK propensity to import within a fixed\n",
    "    exchange rate regime with endogenous UK interest rates, on the UK and US\n",
    "    debt to GDP ratios.'''\n",
    "usdata = [s['BsUS']/s['YUS'] for s in mu0.solutions[5:50]]\n",
    "ukdata = [s['BsUK']/s['YUK'] for s in mu0.solutions[5:50]]\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(usdata, linestyle='-', color='b')\n",
    "axes.plot(ukdata, linestyle='--', linewidth=2, color='g')\n",
    "\n",
    "# add labels\n",
    "plt.text(20, 1.46, 'US debt to GDP ratio')\n",
    "plt.text(20, 1.54, 'UK debt to GDP ratio')\n",
    "fig.text(0.1, -.15, caption);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scenario: Model OPENFIXG, devaluation of pound sterling and increase in UK propensity to import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "xruk0 = create_openfixg_model()\n",
    "xruk0.set_values(openfixg_parameters)\n",
    "xruk0.set_values(openfixg_exogenous)\n",
    "\n",
    "for _ in range(10):\n",
    "    xruk0.solve(iterations=200, threshold=1e-4, method='newton-raphson')\n",
    "\n",
    "xruk0.set_values({'mu0': -2.0})\n",
    "\n",
    "for _ in range(5):\n",
    "    xruk0.solve(iterations=200, threshold=1e-4, method='newton-raphson')\n",
    "\n",
    "xruk0.set_values({'XRUK': 0.8375})\n",
    "\n",
    "for _ in range(35):\n",
    "    xruk0.solve(iterations=200, threshold=1e-4, method='newton-raphson')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 12.4A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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 12.4A  Effect on the UK current account balance and trade balance\n",
    "    of a successful one-step devaluation of the pound sterling, following an\n",
    "    increase in the UK propensity to import, within a fixed exchange rate\n",
    "    regime with endogenous foreign reserves.'''\n",
    "cabdata = [s['CABUK'] for s in xruk0.solutions[5:50]]\n",
    "xidata = [s['XUK'] - s['IMUK'] for s in xruk0.solutions[5:50]]\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(cabdata, linestyle='-', color='b')\n",
    "axes.plot(xidata, linestyle='--', linewidth=2, color='g')\n",
    "\n",
    "# add labels\n",
    "plt.text(20, -0.6, 'UK current account balance')\n",
    "plt.text(20, 0.4, 'UK trade balance')\n",
    "fig.text(0.1, -.15, caption);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 12.4B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "caption = '''\n",
    "    Figure 12.4B  Effect of a successful one-step devaluation of the pound\n",
    "    sterling on UK real GDP, following an increase in the UK propensity to\n",
    "    import, within a fixed exchange rate regime with endogenous foreign reserves.'''\n",
    "ydata = [s['YkUK'] for s in xruk0.solutions[5:50]]\n",
    "xrdata = [s['XRUK'] for s in xruk0.solutions[5:50]]\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(85, 140)\n",
    "axes.plot(ydata, linestyle='-', color='b')\n",
    "\n",
    "plt.text(15, 104, 'UK real rate')\n",
    "\n",
    "axes2 = axes.twinx()\n",
    "axes2.tick_params(top='off')\n",
    "axes2.spines['top'].set_visible(False)\n",
    "axes2.set_ylim(0.83, 1.02)\n",
    "axes2.plot(xrdata, linestyle='--', linewidth=2, color='g')\n",
    "\n",
    "plt.text(20, 0.85, 'Value of UK currency in dollars')\n",
    "\n",
    "# add labels\n",
    "fig.text(0, 1.25, 'Real output')\n",
    "fig.text(1.1, 1.25, 'Exchange 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
}