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🚿 construct list of fields needed from M_, options_, oo_ 

Get fields
time-shift
Willi Mutschler 2020-01-23 10:10:51 +01:00
parent 46c4dea559
commit 1aa3dda449
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2 changed files with 571 additions and 523 deletions
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71
matlab/get_identification_jacobians.m
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@ -88,7 +88,7 @@ function [MEAN, dMEAN, REDUCEDFORM, dREDUCEDFORM, DYNAMIC, dDYNAMIC, MOMENTS, dM
% * identification_numerical_objective (previously thet2tau)
% * vec
% =========================================================================
% Copyright (C) 2010-2019 Dynare Team
% Copyright (C) 2010-2020 Dynare Team
%
% This file is part of Dynare.
%
@ -106,26 +106,32 @@ function [MEAN, dMEAN, REDUCEDFORM, dREDUCEDFORM, DYNAMIC, dDYNAMIC, MOMENTS, dM
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
% =========================================================================
%get options
%get fields from options_ident
no_identification_moments = options_ident.no_identification_moments;
no_identification_minimal = options_ident.no_identification_minimal;
no_identification_spectrum = options_ident.no_identification_spectrum;
order = options_ident.order;
nlags = options_ident.ar;
useautocorr = options_ident.useautocorr;
grid_nbr = options_ident.grid_nbr;
kronflag = options_ident.analytic_derivation_mode;
% set values
params0 = M.params; %values at which to evaluate dynamic, static and param_derivs files
Sigma_e0 = M.Sigma_e; %covariance matrix of exogenous shocks
Corr_e0 = M.Correlation_matrix; %correlation matrix of exogenous shocks
stderr_e0 = sqrt(diag(Sigma_e0)); %standard errors of exogenous shocks
para0 = get_all_parameters(estim_params, M); %get all selected parameters in estimated_params block, stderr and corr come first, then model parameters
if isempty(para0)
% get fields from M
endo_nbr = M.endo_nbr;
exo_nbr = M.exo_nbr;
fname = M.fname;
lead_lag_incidence = M.lead_lag_incidence;
nspred = M.nspred;
nstatic = M.nstatic;
params = M.params;
Sigma_e = M.Sigma_e;
stderr_e = sqrt(diag(Sigma_e));
% set all selected values: stderr and corr come first, then model parameters
xparam1 = get_all_parameters(estim_params, M); %try using estimated_params block
if isempty(xparam1)
%if there is no estimated_params block, consider all stderr and all model parameters, but no corr parameters
para0 = [stderr_e0', params0'];
xparam1 = [stderr_e', params'];
end
%get numbers/lengths of vectors
@ -134,21 +140,16 @@ stderrparam_nbr = length(indpstderr);
corrparam_nbr = size(indpcorr,1);
totparam_nbr = stderrparam_nbr + corrparam_nbr + modparam_nbr;
obs_nbr = length(indvobs);
exo_nbr = M.exo_nbr;
endo_nbr = M.endo_nbr;
nspred = M.nspred;
nstatic = M.nstatic;
indvall = (1:endo_nbr)'; %index for all endogenous variables
d2flag = 0; % do not compute second parameter derivatives
% Get Jacobians (wrt selected params) of steady state, dynamic model derivatives and perturbation solution matrices for all endogenous variables
oo.dr.derivs = get_perturbation_params_derivs(M, options, estim_params, oo, indpmodel, indpstderr, indpcorr, d2flag);
[I,~] = find(M.lead_lag_incidence'); %I is used to select nonzero columns of the Jacobian of endogenous variables in dynamic model files
[I,~] = find(lead_lag_incidence'); %I is used to select nonzero columns of the Jacobian of endogenous variables in dynamic model files
yy0 = oo.dr.ys(I); %steady state of dynamic (endogenous and auxiliary variables) in lead_lag_incidence order
Yss = oo.dr.ys(oo.dr.order_var); % steady state in DR order
if order == 1
[~, g1 ] = feval([M.fname,'.dynamic'], yy0, oo.exo_steady_state', params0, oo.dr.ys, 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
@ -156,7 +157,7 @@ if order == 1
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
REDUCEDFORM = [Yss;
vec(oo.dr.ghx);
dyn_vech(oo.dr.ghu*Sigma_e0*transpose(oo.dr.ghu))]; %in DR order
dyn_vech(oo.dr.ghu*Sigma_e*transpose(oo.dr.ghu))]; %in DR order
dREDUCEDFORM = zeros(endo_nbr*nspred+endo_nbr*(endo_nbr+1)/2, totparam_nbr);
for j=1:totparam_nbr
dREDUCEDFORM(:,j) = [vec(oo.dr.derivs.dghx(:,:,j));
@ -165,7 +166,7 @@ if order == 1
dREDUCEDFORM = [ [zeros(endo_nbr, stderrparam_nbr+corrparam_nbr) oo.dr.derivs.dYss]; dREDUCEDFORM ]; % add steady state
elseif order == 2
[~, g1, g2 ] = feval([M.fname,'.dynamic'], yy0, oo.exo_steady_state', params0, oo.dr.ys, 1);
[~, g1, g2 ] = 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
%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;
@ -176,7 +177,7 @@ elseif order == 2
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
REDUCEDFORM = [Yss;
vec(oo.dr.ghx);
dyn_vech(oo.dr.ghu*Sigma_e0*transpose(oo.dr.ghu));
dyn_vech(oo.dr.ghu*Sigma_e*transpose(oo.dr.ghu));
vec(oo.dr.ghxx);
vec(oo.dr.ghxu);
vec(oo.dr.ghuu);
@ -192,7 +193,7 @@ elseif order == 2
end
dREDUCEDFORM = [ [zeros(endo_nbr, stderrparam_nbr+corrparam_nbr) oo.dr.derivs.dYss]; dREDUCEDFORM ]; % add steady state
elseif order == 3
[~, g1, g2, g3 ] = feval([M.fname,'.dynamic'], yy0, oo.exo_steady_state', params0, oo.dr.ys, 1);
[~, g1, g2, g3 ] = 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
%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;
@ -205,7 +206,7 @@ elseif order == 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
REDUCEDFORM = [Yss;
vec(oo.dr.ghx);
dyn_vech(oo.dr.ghu*Sigma_e0*transpose(oo.dr.ghu));
dyn_vech(oo.dr.ghu*Sigma_e*transpose(oo.dr.ghu));
vec(oo.dr.ghxx); vec(oo.dr.ghxu); vec(oo.dr.ghuu); vec(oo.dr.ghs2);
vec(oo.dr.ghxxx); vec(oo.dr.ghxxu); vec(oo.dr.ghxuu); vec(oo.dr.ghuuu); vec(oo.dr.ghxss); vec(oo.dr.ghuss)]; %in DR order
dREDUCEDFORM = zeros(size(REDUCEDFORM,1)-endo_nbr, totparam_nbr);
@ -250,14 +251,11 @@ end
% zhat = A*zhat(-1) + B*xi, where zhat = z - E(z)
% yhat = C*zhat(-1) + D*xi, where yhat = y - E(y)
if ~no_identification_moments
MOMENTS = identification_numerical_objective(para0, 1, estim_params, M, oo, options, indpmodel, indpstderr, indpcorr, indvobs, useautocorr, nlags, grid_nbr); %[outputflag=1]
MOMENTS = identification_numerical_objective(xparam1, 1, estim_params, M, oo, options, indpmodel, indpstderr, indpcorr, indvobs, useautocorr, nlags, grid_nbr); %[outputflag=1]
MOMENTS = [MEAN; MOMENTS];
if kronflag == -1
%numerical derivative of autocovariogram
dMOMENTS = fjaco(str2func('identification_numerical_objective'), para0, 1, estim_params, M, oo, options, indpmodel, indpstderr, indpcorr, indvobs, useautocorr, nlags, grid_nbr); %[outputflag=1]
M.params = params0; %make sure values are set back
M.Sigma_e = Sigma_e0; %make sure values are set back
M.Correlation_matrix = Corr_e0 ; %make sure values are set back
dMOMENTS = fjaco(str2func('identification_numerical_objective'), xparam1, 1, estim_params, M, oo, options, indpmodel, indpstderr, indpcorr, indvobs, useautocorr, nlags, grid_nbr); %[outputflag=1]
dMOMENTS = [dMEAN; dMOMENTS]; %add Jacobian of steady state of VAROBS variables
else
dMOMENTS = zeros(obs_nbr + obs_nbr*(obs_nbr+1)/2 + nlags*obs_nbr^2 , totparam_nbr);
@ -334,19 +332,19 @@ if ~no_identification_spectrum
% tpos = exp( sqrt(-1)*freqs); %positive Fourier frequencies
% tneg = exp(-sqrt(-1)*freqs); %negative Fourier frequencies
% IA = eye(size(A,1));
% IE = eye(M.exo_nbr);
% IE = eye(exo_nbr);
% mathp_col1 = NaN(length(freqs),obs_nbr^2); mathp_col2 = mathp_col1; mathp_col3 = mathp_col1; mathp_col4 = mathp_col1;
% for ig = 1:length(freqs)
% %method 1: as in UnivariateSpectralDensity.m
% f_omega =(1/(2*pi))*( [(IA-A*tneg(ig))\B;IE]*M.Sigma_e*[B'/(IA-A'*tpos(ig)) IE]); % state variables
% f_omega =(1/(2*pi))*( [(IA-A*tneg(ig))\B;IE]*Sigma_e*[B'/(IA-A'*tpos(ig)) IE]); % state variables
% g_omega1 = [C*tneg(ig) D]*f_omega*[C'*tpos(ig); D']; % selected variables
% %method 2: as in UnivariateSpectralDensity.m but simplified algebraically
% g_omega2 = (1/(2*pi))*( C*((tpos(ig)*IA-A)\(B*M.Sigma_e*B'))*((tneg(ig)*IA-A')\(C')) + D*M.Sigma_e*B'*((tneg(ig)*IA-A')\(C')) + C* ((tpos(ig)*IA-A)\(B*M.Sigma_e*D')) + D*M.Sigma_e*D' );
% g_omega2 = (1/(2*pi))*( C*((tpos(ig)*IA-A)\(B*Sigma_e*B'))*((tneg(ig)*IA-A')\(C')) + D*Sigma_e*B'*((tneg(ig)*IA-A')\(C')) + C* ((tpos(ig)*IA-A)\(B*Sigma_e*D')) + D*Sigma_e*D' );
% %method 3: use transfer function note that ' is the complex conjugate transpose operator i.e. transpose(ffneg')==ffpos
% Transferfct = D+C*((tpos(ig)*IA-A)\B);
% g_omega3 = (1/(2*pi))*(Transferfct*M.Sigma_e*Transferfct');
% g_omega3 = (1/(2*pi))*(Transferfct*Sigma_e*Transferfct');
% %method 4: kronecker products
% g_omega4 = (1/(2*pi))*( kron( D+C*((tneg(ig)^(-1)*IA-A)\B) , D+C*((tneg(ig)*IA-A)\B) )*M.Sigma_e(:));
% g_omega4 = (1/(2*pi))*( kron( D+C*((tneg(ig)^(-1)*IA-A)\B) , D+C*((tneg(ig)*IA-A)\B) )*Sigma_e(:));
% % store as matrix row
% mathp_col1(ig,:) = (g_omega1(:))'; mathp_col2(ig,:) = (g_omega2(:))'; mathp_col3(ig,:) = (g_omega3(:))'; mathp_col4(ig,:) = g_omega4;
% end
@ -362,10 +360,7 @@ if ~no_identification_spectrum
IA = eye(size(A,1));
if kronflag == -1
%numerical derivative of spectral density
dOmega_tmp = fjaco(str2func('identification_numerical_objective'), para0, 2, estim_params, M, oo, options, indpmodel, indpstderr, indpcorr, indvobs, useautocorr, nlags, grid_nbr); %[outputflag=2]
M.params = params0; %make sure values are set back
M.Sigma_e = Sigma_e0; %make sure values are set back
M.Correlation_matrix = Corr_e0 ; %make sure values are set back
dOmega_tmp = fjaco(str2func('identification_numerical_objective'), xparam1, 2, estim_params, M, oo, options, indpmodel, indpstderr, indpcorr, indvobs, useautocorr, nlags, grid_nbr); %[outputflag=2]
kk = 0;
for ig = 1:length(freqs)
kk = kk+1;
@ -483,7 +478,7 @@ if ~no_identification_minimal
kron(Inu,minB);
zeros(obs_nbr*minnx,exo_nbr^2);
kron(Inu,minD);
-2*Enu*kron(Sigma_e0,Inu)];
-2*Enu*kron(Sigma_e,Inu)];
dMINIMAL = full([KomunjerNg_DL KomunjerNg_DT KomunjerNg_DU]);
%add Jacobian of steady state (here we also allow for higher-order perturbation, i.e. only the mean provides additional restrictions
dMINIMAL = [dMEAN zeros(obs_nbr,minnx^2+exo_nbr^2); dMINIMAL];

1023
matlab/get_perturbation_params_derivs.m
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