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