stepan
/
dynare
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Merge branch 'ident_opt' into 'master' 

identification: support optimal policy

See merge request Dynare/dynare!1852
time-shift
Sébastien Villemot 2021-05-25 13:07:06 +00:00
parent 5e36b688ee 456dc86ad1
commit c78e37290a
8 changed files with 38 additions and 19 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

8
matlab/dynare_identification.m
View File

@ -321,6 +321,14 @@ options_ident = set_default_option(options_ident,'analytic_derivation_mode', opt
% 1: kronecker products method to compute analytical derivatives as in Iskrev (2010) (only for order=1)
% -1: numerical two-sided finite difference method to compute numerical derivatives of all identification Jacobians using function identification_numerical_objective.m (previously thet2tau.m)
% -2: numerical two-sided finite difference method to compute numerically dYss, dg1, dg2, dg3, d2Yss and d2g1, the identification Jacobians are then computed analytically as with 0
if options_.discretionary_policy || options_.ramsey_policy
if options_ident.analytic_derivation_mode~=-1
fprintf('dynare_identification: discretionary_policy and ramsey_policy require analytic_derivation_mode=-1. Resetting the option.')
options_ident.analytic_derivation_mode=-1;
end
end
options_.analytic_derivation_mode = options_ident.analytic_derivation_mode; %overwrite setting in options_
% initialize persistent variables in prior_draw

24
matlab/get_identification_jacobians.m
View File

@ -159,9 +159,9 @@ if order == 1
[~, g1 ] = feval([fname,'.dynamic'], yy0, oo.exo_steady_state', params, oo.dr.ys, 1);
%g1 is [endo_nbr by yy0ex0_nbr first derivative (wrt all dynamic variables) of dynamic model equations, i.e. df/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
DYNAMIC = [Yss;
vec(g1(oo.dr.order_var,:))]; %add steady state and put rows of g1 in DR order
vec(g1)]; %add steady state and put rows of g1 in DR order
dDYNAMIC = [oo.dr.derivs.dYss;
reshape(oo.dr.derivs.dg1(oo.dr.order_var,:,:),size(oo.dr.derivs.dg1,1)*size(oo.dr.derivs.dg1,2),size(oo.dr.derivs.dg1,3)) ]; %reshape dg1 in DR order and add steady state
reshape(oo.dr.derivs.dg1,size(oo.dr.derivs.dg1,1)*size(oo.dr.derivs.dg1,2),size(oo.dr.derivs.dg1,3)) ]; %reshape dg1 in DR order and add steady state
REDUCEDFORM = [Yss;
vec(oo.dr.ghx);
dyn_vech(oo.dr.ghu*Sigma_e*transpose(oo.dr.ghu))]; %in DR order
@ -177,11 +177,11 @@ elseif order == 2
%g1 is [endo_nbr by yy0ex0_nbr first derivative (wrt all dynamic variables) of dynamic model equations, i.e. df/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
%g2 is [endo_nbr by yy0ex0_nbr^2] second derivative (wrt all dynamic variables) of dynamic model equations, i.e. d(df/dyy0ex0)/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
DYNAMIC = [Yss;
vec(g1(oo.dr.order_var,:));
vec(g2(oo.dr.order_var,:))]; %add steady state and put rows of g1 and g2 in DR order
vec(g1);
vec(g2)]; %add steady state and put rows of g1 and g2 in DR order
dDYNAMIC = [oo.dr.derivs.dYss;
reshape(oo.dr.derivs.dg1(oo.dr.order_var,:,:),size(oo.dr.derivs.dg1,1)*size(oo.dr.derivs.dg1,2),size(oo.dr.derivs.dg1,3)); %reshape dg1 in DR order
reshape(oo.dr.derivs.dg2(oo.dr.order_var,:),size(oo.dr.derivs.dg1,1)*size(oo.dr.derivs.dg1,2)^2,size(oo.dr.derivs.dg1,3))]; %reshape dg2 in DR order
reshape(oo.dr.derivs.dg1,size(oo.dr.derivs.dg1,1)*size(oo.dr.derivs.dg1,2),size(oo.dr.derivs.dg1,3)); %reshape dg1 in DR order
reshape(oo.dr.derivs.dg2,size(oo.dr.derivs.dg1,1)*size(oo.dr.derivs.dg1,2)^2,size(oo.dr.derivs.dg1,3))]; %reshape dg2 in DR order
REDUCEDFORM = [Yss;
vec(oo.dr.ghx);
dyn_vech(oo.dr.ghu*Sigma_e*transpose(oo.dr.ghu));
@ -204,13 +204,13 @@ elseif order == 3
%g1 is [endo_nbr by yy0ex0_nbr first derivative (wrt all dynamic variables) of dynamic model equations, i.e. df/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
%g2 is [endo_nbr by yy0ex0_nbr^2] second derivative (wrt all dynamic variables) of dynamic model equations, i.e. d(df/dyy0ex0)/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
DYNAMIC = [Yss;
vec(g1(oo.dr.order_var,:));
vec(g2(oo.dr.order_var,:));
vec(g3(oo.dr.order_var,:))]; %add steady state and put rows of g1 and g2 in DR order
vec(g1);
vec(g2);
vec(g3)]; %add steady state and put rows of g1 and g2 in DR order
dDYNAMIC = [oo.dr.derivs.dYss;
reshape(oo.dr.derivs.dg1(oo.dr.order_var,:,:),size(oo.dr.derivs.dg1,1)*size(oo.dr.derivs.dg1,2),size(oo.dr.derivs.dg1,3)); %reshape dg1 in DR order
reshape(oo.dr.derivs.dg2(oo.dr.order_var,:),size(oo.dr.derivs.dg1,1)*size(oo.dr.derivs.dg1,2)^2,size(oo.dr.derivs.dg1,3));
reshape(oo.dr.derivs.dg2(oo.dr.order_var,:),size(oo.dr.derivs.dg1,1)*size(oo.dr.derivs.dg1,2)^2,size(oo.dr.derivs.dg1,3))]; %reshape dg3 in DR order
reshape(oo.dr.derivs.dg1,size(oo.dr.derivs.dg1,1)*size(oo.dr.derivs.dg1,2),size(oo.dr.derivs.dg1,3)); %reshape dg1 in DR order
reshape(oo.dr.derivs.dg2,size(oo.dr.derivs.dg1,1)*size(oo.dr.derivs.dg1,2)^2,size(oo.dr.derivs.dg1,3));
reshape(oo.dr.derivs.dg2,size(oo.dr.derivs.dg1,1)*size(oo.dr.derivs.dg1,2)^2,size(oo.dr.derivs.dg1,3))]; %reshape dg3 in DR order
REDUCEDFORM = [Yss;
vec(oo.dr.ghx);
dyn_vech(oo.dr.ghu*Sigma_e*transpose(oo.dr.ghu));

12
matlab/get_perturbation_params_derivs.m
View File

@ -344,9 +344,17 @@ if analytic_derivation_mode == -1
%Parameter Jacobian of dynamic model derivatives (wrt selected model parameters only)
dYss_g = fjaco(numerical_objective_fname, modparam1, 'dynamic_model', estim_params_model, M, oo, options);
ind_Yss = 1:endo_nbr;
ind_g1 = ind_Yss(end) + (1:endo_nbr*yy0ex0_nbr);
if options.discretionary_policy || options.ramsey_policy
ind_g1 = ind_Yss(end) + (1:M.eq_nbr*yy0ex0_nbr);
else
ind_g1 = ind_Yss(end) + (1:endo_nbr*yy0ex0_nbr);
end
DERIVS.dYss = dYss_g(ind_Yss, :); %in tensor notation, wrt selected model parameters only
DERIVS.dg1 = reshape(dYss_g(ind_g1,:),[endo_nbr, yy0ex0_nbr, modparam_nbr]); %in tensor notation, wrt selected model parameters only
if options.discretionary_policy || options.ramsey_policy
DERIVS.dg1 = reshape(dYss_g(ind_g1,:),[M.eq_nbr, yy0ex0_nbr, modparam_nbr]); %in tensor notation, wrt selected model parameters only
else
DERIVS.dg1 = reshape(dYss_g(ind_g1,:),[endo_nbr, yy0ex0_nbr, modparam_nbr]); %in tensor notation, wrt selected model parameters only
end
if order > 1
ind_g2 = ind_g1(end) + (1:endo_nbr*yy0ex0_nbr^2);
DERIVS.dg2 = reshape(sparse(dYss_g(ind_g2,:)),[endo_nbr, yy0ex0_nbr^2*modparam_nbr]); %blockwise in matrix notation, i.e. [dg2_dp1 dg2_dp2 ...], where dg2_dpj has dimension endo_nbr by yy0ex0_nbr^2

2
matlab/get_perturbation_params_derivs_numerical_objective.m
View File

@ -51,7 +51,7 @@ function [out,info] = get_perturbation_params_derivs_numerical_objective(params,
%% Update stderr, corr and model parameters and compute perturbation approximation and steady state with updated parameters
M = set_all_parameters(params,estim_params,M);
[~,info,M,options,oo] = resol(0,M,options,oo);
[~,info,M,options,oo] = compute_decision_rules(M,options,oo);
Sigma_e = M.Sigma_e;
if info(1) > 0

2
matlab/identification_analysis.m
View File

@ -133,7 +133,7 @@ no_identification_minimal = options_ident.no_identification_minimal;
no_identification_spectrum = options_ident.no_identification_spectrum;
%Compute linear approximation and fill dr structure
[oo_.dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
[oo_.dr,info,M_,options_,oo_] = compute_decision_rules(M_,options_,oo_);
if info(1) == 0 %no errors in solution
% Compute parameter Jacobians for identification analysis

2
matlab/identification_numerical_objective.m
View File

@ -76,7 +76,7 @@ else
end
%% compute Kalman transition matrices and steady state with updated parameters
[~,info,M,options,oo] = resol(0,M,options,oo);
[~,info,M,options,oo] = compute_decision_rules(M,options,oo);
options = rmfield(options,'options_ident');
pruned = pruned_state_space_system(M, options, oo.dr, indvar, nlags, useautocorr, 0);

4
tests/discretionary_policy/dennis_1_estim.mod
View File

@ -36,8 +36,10 @@ estimated_params;
end;
options_.plot_priors=0;
estimation(order = 1, datafile = dennis_simul, mh_replic = 2000, mh_nblocks=1,smoother,bayesian_irf,moments_varendo) y i pi pi_c q;
estimation(order = 1, datafile = dennis_simul, mh_replic = 2000, mh_nblocks=1,smoother,bayesian_irf,moments_varendo, conditional_variance_decomposition=[1,2]) y i pi pi_c q;
if max(abs(oo_.posterior.optimization.mode - [1; 0.3433])) > 0.025
error('Posterior mode too far from true parameter values');
end
identification;

3
tests/optimal_policy/Ramsey/Ramsey_Example_estimation.mod
View File

@ -222,7 +222,7 @@ end;
ramsey_model(instruments=(R),planner_discount=beta,planner_discount_latex_name=$\beta$);
//conduct stochastic simulations of the Ramsey problem
stoch_simul(order=1,irf=20,periods=500) pi_ann log_h R_ann log_C Z r_real;
stoch_simul(TeX,order=1,irf=20,periods=500) pi_ann log_h R_ann log_C Z r_real;
evaluate_planner_objective;
@# if Estimation_under_Ramsey==1
@ -234,6 +234,7 @@ end;
varobs log_C;
estimation(datafile=ramsey_simulation,mode_compute=5,mh_nblocks=1,mh_replic=0);
identification(parameter_set=posterior_mode);
@# endif
@# endif
@# endif