{
"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 pysolve3.model import Model\n",
"from pysolve3.utils import is_close,round_solution\n",
"\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model OPENFIX"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def create_openfix_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('BcbsUKUS', desc='Bills issued by the US, supplied to 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('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('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('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",
" 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",
" 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",
" model.add('BsUKUS = XRUK*BdUKUS') # 12.89F\n",
" # 12.90F : Supply of UK bills to us\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",
"openfix_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",
"openfix_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 OPENFIX, baseline"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"baseline = create_openfix_model()\n",
"baseline.set_values(openfix_parameters)\n",
"baseline.set_values(openfix_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",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Scenario: Model OPENFIX, increase US propensity to import"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"eps0 = create_openfix_model()\n",
"eps0.set_values(openfix_parameters)\n",
"eps0.set_values(openfix_exogenous)\n",
"\n",
"for _ in range(10):\n",
" eps0.solve(iterations=200, threshold=1e-4, method='broyden')\n",
"\n",
"eps0.set_values({'eps0': -2.0})\n",
"\n",
"for _ in range(90):\n",
" eps0.solve(iterations=200, threshold=1e-4, method='broyden')\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###### Figure 12.1A"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"caption = '''\n",
" Figure 12.1A Effect of an increase in the US propensity to import on UK variables,\n",
" within a fixed exchange rate regime with endogenous foreign reserves: net accumulation\n",
" of financial assets, current account balance, trade balance, and government budget\n",
" balance.'''\n",
"cabdata = [s['CABUK'] for s in eps0.solutions[5:50]]\n",
"xidata = [s['XUK'] - s['IMUK'] for s in eps0.solutions[5:50]]\n",
"psbrdata = [-s['PSBRUK'] for s in eps0.solutions[5:50]]\n",
"nafadata = [s['NAFAUK'] for s in eps0.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=False, right=False)\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(psbrdata, linestyle=':', linewidth=2, color='r')\n",
"axes.plot(nafadata, linestyle='-.', linewidth=2, color='b')\n",
"\n",
"# add labels\n",
"plt.text(10, 1.05, 'UK current account balance')\n",
"plt.text(25, 0.6, 'UK trade balance')\n",
"plt.text(28, 0.9, 'UK government')\n",
"plt.text(28, 0.8, 'budget balance')\n",
"plt.text(20, 0.2, 'UK NAFA')\n",
"fig.text(0.1, -.15, caption);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###### Figure 12.1B"
]
},
{
"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": {},
"output_type": "display_data"
}
],
"source": [
"caption = '''\n",
" Figure 12.1B Effect of an increase in the US propensity to import, within\n",
" a fixed exchange rate regime with endogenous foreign reserves, on the\n",
" UK current account balance and elements of the balance sheet of the \n",
" Bank of England (the UK central bank): change in foreign reserves, stock\n",
" of money, holdings of domestic Treasury bills.'''\n",
"cabdata = list()\n",
"ukukdata = list()\n",
"hukdata = list()\n",
"ukusdata = list()\n",
"\n",
"for i in range(5, 50):\n",
" s = eps0.solutions[i]\n",
" s_1 = eps0.solutions[i-1]\n",
"\n",
" cabdata.append(s['CABUK'])\n",
" ukukdata.append(s['BcbdUKUK'] - s_1['BcbdUKUK'])\n",
" hukdata.append(s['HdUK'] - s_1['HdUK'])\n",
" ukusdata.append(s['BcbdUKUS'] - s_1['BcbdUKUS'])\n",
"\n",
"fig = plt.figure()\n",
"axes = fig.add_axes([0.1, 0.1, 1.1, 1.1])\n",
"axes.tick_params(top=False, right=False)\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(ukukdata, linestyle='--', linewidth=2, color='g')\n",
"axes.plot(hukdata, linestyle=':', linewidth=2, color='r')\n",
"axes.plot(ukusdata, linestyle='-.', linewidth=2, color='b')\n",
"\n",
"# add labels\n",
"plt.text(10, 1.05, 'UK current account balance')\n",
"plt.text(5, -0.8, 'Change in the')\n",
"plt.text(5, -1.0, 'Bank of England holdings')\n",
"plt.text(5, -1.2, 'of domestic Treasury bills')\n",
"plt.text(15, -0.2, 'Change in the UK stock of money')\n",
"plt.text(15, 0.5, 'Change in the Bank of England foreign reserves')\n",
"fig.text(0.1, -.2, caption);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###### Figure 12.1C"
]
},
{
"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": {},
"output_type": "display_data"
}
],
"source": [
"caption = '''\n",
" Figure 12.1C Effect of an increase in the US propensity to import on the US debt\n",
" to GDP ratio and on the UK debt to income ratio, within a fixed exchange rate\n",
" regime with endogenous foreign reserves.'''\n",
"usdata = [s['BsUS']/s['YUS'] for s in eps0.solutions[5:50]]\n",
"ukdata = [s['BsUK']/s['YUK'] for s in eps0.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=False, right=False)\n",
"axes.spines['top'].set_visible(False)\n",
"axes.spines['right'].set_visible(False)\n",
"#axes.set_ylim(37, 45)\n",
"\n",
"axes.plot(ukdata, linestyle='-', color='b')\n",
"axes.plot(usdata, linestyle='--', linewidth=2, color='g')\n",
"\n",
"# add labels\n",
"plt.text(22, 1.4, 'UK debt to GDP ratio')\n",
"plt.text(22, 1.6, 'US debt to GDP ratio')\n",
"fig.text(0.1, -.1, caption);"
]
},
{
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"source": []
}
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