Add unit tests for correctness of identification results when only standard deviations are estimated
Compares results for standard deviations specified with varexo to results when specified as deep parametertime-shift
Johannes Pfeifer
parent
446b1f6dc1
commit
fbaf27493c
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@ -125,6 +125,8 @@ MODFILES = \
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identification/kim/kim2.mod \
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identification/as2007/as2007.mod \
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identification/ident_unit_root/ident_unit_root.mod \
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identification/rbc_ident/rbc_ident_std_as_structural_par.mod \
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identification/rbc_ident/rbc_ident_varexo_only.mod \
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simul/example1.mod \
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simul/Solow_no_varexo.mod \
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simul/simul_ZLB_purely_forward.mod \
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@ -305,6 +307,8 @@ deterministic_simulations/rbc_det_exo_lag_2c.o.trs: deterministic_simulations/rb
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initval_file/ramst_initval_file.m.trs: initval_file/ramst_initval_file_data.m.tls
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initval_file/ramst_initval_file.o.trs: initval_file/ramst_initval_file_data.o.tls
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identification/rbc_ident/rbc_ident_varexo_only.m.trs: identification/rbc_ident/rbc_ident_std_as_structural_par.m.trs
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identification/rbc_ident/rbc_ident_varexo_only.o.trs: identification/rbc_ident/rbc_ident_std_as_structural_par.o.trs
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# Matlab TRS Files
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@ -0,0 +1,95 @@
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% Real Business Cycle Model
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close all;
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%----------------------------------------------------------------
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% 1. Defining variables
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%----------------------------------------------------------------
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var y c k l x z g ysim xsim;
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varexo eps_z eps_g;
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parameters gn gz betta delta psi sigma theta rho_z eps_z_sigma rho_g eps_g_sigma q12 zbar gbar;
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%----------------------------------------------------------------
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% 2. Calibration
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%----------------------------------------------------------------
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gn = 0.00243918275778010;
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gz = 0.00499789993972673;
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betta = 0.9722^(1/4)/(1+gz);
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delta = 1-(1-0.0464)^(1/4);
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psi = 2.24;
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sigma = 1.000001;
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theta = 0.35;
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zbar = 0.0023;
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gbar = -0.0382;
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rho_z = 0.8;
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sig_z = 0.0086;
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rho_g = 0.8;
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sig_g = 0.0248;
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q12 = -0.0002;
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%----------------------------------------------------------------
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% 3. Model
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%----------------------------------------------------------------
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model;
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% Intratemporal Optimality Condition
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(psi*c)/(1-l) = (1-theta)*(k(-1)^theta)*(l^(-theta))*(exp(z)^(1-theta));
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% Intertemporal Optimality Condition
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(c^(-sigma))*((1-l)^(psi*(1-sigma))) = betta*((c(+1))^(-sigma))*((1-l(+1))^(psi*(1-sigma)))*(theta*(k^(theta-1))*((exp(z(+1))*l(+1))^(1-theta))+(1-delta));
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% Aggregate Resource Constraint
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y = c + exp(g) + x;
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% Capital Accumulation Law
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(1+gz)*(1+gn)*k = (1-delta)*k(-1) + x;
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% Production Function
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y = (k(-1)^theta)*((exp(z)*l)^(1-theta));
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% Stochastic Processes
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z = zbar + rho_z*z(-1) + eps_z_sigma*eps_z;
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g = gbar + rho_g*g(-1) + eps_g_sigma*eps_g;
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ysim = y;
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xsim = x;
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end;
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%----------------------------------------------------------------
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% 4. Computation
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%----------------------------------------------------------------
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shocks;
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var eps_z ; stderr 1;
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var eps_g ; stderr 1;
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end;
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initval;
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z = 0;
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k = (betta*theta)^(1/(1-theta))*exp(zbar/(1-rho_z));
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c = (1-betta*theta)/(betta*theta)*k;
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y = exp(zbar/(1-rho_z))^(1-theta)*k^theta;
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l = k/(((1-betta*(1-delta))/(betta*theta*(exp(zbar/(1-rho_z))^(1-theta))))^(1/(theta-1)));
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end;
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estimated_params;
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eps_z_sigma, 0.0001, 0,10;
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eps_g_sigma, 0.0001, 0,10;
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% betta, 0.9722^(1/4)/(1+gz),0,1;
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%corr eps_z, eps_g, 0.0001, -1,1;
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end;
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varobs ysim xsim;
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identification(advanced=1);
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@ -0,0 +1,105 @@
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% Real Business Cycle Model
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close all;
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%----------------------------------------------------------------
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% 1. Defining variables
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%----------------------------------------------------------------
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var y c k l x z g ysim xsim;
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varexo eps_z eps_g;
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parameters gn gz betta delta psi sigma theta rho_z sig_z rho_g sig_g q12 zbar gbar;
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%----------------------------------------------------------------
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% 2. Calibration
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%----------------------------------------------------------------
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gn = 0.00243918275778010;
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gz = 0.00499789993972673;
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betta = 0.9722^(1/4)/(1+gz);
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delta = 1-(1-0.0464)^(1/4);
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psi = 2.24;
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sigma = 1.000001;
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theta = 0.35;
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zbar = 0.0023;
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gbar = -0.0382;
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rho_z = 0.8;
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sig_z = 0.0086;
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rho_g = 0.8;
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sig_g = 0.0248;
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q12 = -0.0002;
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%----------------------------------------------------------------
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% 3. Model
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%----------------------------------------------------------------
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model;
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% Intratemporal Optimality Condition
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(psi*c)/(1-l) = (1-theta)*(k(-1)^theta)*(l^(-theta))*(exp(z)^(1-theta));
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% Intertemporal Optimality Condition
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(c^(-sigma))*((1-l)^(psi*(1-sigma))) = betta*((c(+1))^(-sigma))*((1-l(+1))^(psi*(1-sigma)))*(theta*(k^(theta-1))*((exp(z(+1))*l(+1))^(1-theta))+(1-delta));
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% Aggregate Resource Constraint
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y = c + exp(g) + x;
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% Capital Accumulation Law
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(1+gz)*(1+gn)*k = (1-delta)*k(-1) + x;
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% Production Function
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y = (k(-1)^theta)*((exp(z)*l)^(1-theta));
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% Stochastic Processes
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z = zbar + rho_z*z(-1) + eps_z;
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g = gbar + rho_g*g(-1) + eps_g;
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ysim = y;
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xsim = x;
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end;
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%----------------------------------------------------------------
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% 4. Computation
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%----------------------------------------------------------------
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shocks;
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var eps_z ; stderr sig_z;
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var eps_g ; stderr sig_g;
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end;
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initval;
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z = 0;
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k = (betta*theta)^(1/(1-theta))*exp(zbar/(1-rho_z));
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c = (1-betta*theta)/(betta*theta)*k;
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y = exp(zbar/(1-rho_z))^(1-theta)*k^theta;
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l = k/(((1-betta*(1-delta))/(betta*theta*(exp(zbar/(1-rho_z))^(1-theta))))^(1/(theta-1)));
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end;
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estimated_params;
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stderr eps_z, 0.0001, 0,10;
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stderr eps_g, 0.0001, 0,10;
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% betta, 0.9,0,1;
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%corr eps_z, eps_g, 0.0001, -1,1;
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end;
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varobs ysim xsim;
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identification(advanced=1);
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temp=load([M_.dname filesep 'identification' filesep M_.fname '_ML_Starting_value_identif'])
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temp_comparison=load(['rbc_ident_std_as_structural_par' filesep 'identification' filesep 'rbc_ident_std_as_structural_par' '_ML_Starting_value_identif'])
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if max(abs(temp.idehess_point.ide_strength_J - temp_comparison.idehess_point.ide_strength_J))>1e-8 ||...
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max(abs(temp.idehess_point.deltaM - temp_comparison.idehess_point.deltaM))>1e-8 ||...
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max(abs(temp.idemoments_point.GAM- temp.idemoments_point.GAM))>1e-8 ||...
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max(abs(temp.idemoments_point.GAM- temp.idemoments_point.GAM))>1e-8
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error('Results do not match')
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end
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