{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Monetary Economics: 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 OPENFIXR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_openfixr_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('BcbsUKUK', desc='Bills issued by the UK, 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('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('RUK', desc='Interest rate on the UK bills')\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('BcbsUKUS', desc='Bills issued by the US, 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('GkUK', desc='Real government expenditure in the UK')\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('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",
    "    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",
    "    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",
    "    # 12.68R : Demand for US bills in UK - now solved for RUK\n",
    "    model.add('RUK = (lambda20 + lambda22*(RUS + DXREUS) - BdUKUS/VUK)/lambda21')\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",
    "    # model.add('BcbsUKUK = BsUK - BsUKUK - BsUSUK')\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",
    "    # 12.89R : Bills supply from UK to US - now solved fro BdUKUS\n",
    "    model.add('BdUKUS = BsUKUS*XRUS')\n",
    "    # 12.90R : Supply of UK bills to US, now solved for BsUKUS\n",
    "    model.add('BsUKUS = BsUS - BsUSUS - BcbdUSUS - BcbsUKUS')\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",
    "openfixr_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",
    "openfixr_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",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scenario: Model OPENFIXR, baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "baseline = create_openfixr_model()\n",
    "baseline.set_values(openfixr_parameters)\n",
    "baseline.set_values(openfixr_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 OPENFIXR, increase in the UK propensity to import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu0 = create_openfixr_model()\n",
    "mu0.set_values(openfixr_parameters)\n",
    "mu0.set_values(openfixr_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 12.2A"
   ]
  },
  {
   "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 12.2A  Effect of an increase in the UK propensity to import within a fixed\n",
    "    exchange rate regime with endogenous UK interest rates, on UK variables: capital\n",
    "    account balance, trade balance, and current account 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, -4, 'UK current')\n",
    "plt.text(25, -5, 'account balance')\n",
    "plt.text(30, 0, 'UK trade balance')\n",
    "plt.text(25, 5, 'UK capital')\n",
    "plt.text(25, 4, 'account balance')\n",
    "fig.text(0.1, -.1, caption);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 12.2B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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 12.2B  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 interest rate\n",
    "    and on the UK debt to GDP rato.'''\n",
    "gdpdata = [s['BsUK']/s['YUK'] for s in mu0.solutions[5:50]]\n",
    "rdata = [s['RUK'] 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",
    "axes.set_ylim(1.35, 2.8)\n",
    "\n",
    "axes.plot(gdpdata, linestyle='-', color='b')\n",
    "plt.text(10, 1.7, 'UK debt to GDP ratio')\n",
    "\n",
    "axes2 = axes.twinx()\n",
    "axes2.spines['top'].set_visible(False)\n",
    "axes2.set_ylim(0.025, 0.115)\n",
    "axes2.plot(rdata, linestyle='--', linewidth=2, color='g')\n",
    "plt.text(25, 0.04, 'UK interest rate')\n",
    "# add labels\n",
    "fig.text(0, 1.25, 'Debt to GDP ratio')\n",
    "fig.text(1.1, 1.25, 'Interest rate')\n",
    "fig.text(0.1, -.1, 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",
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