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Revert "More fixing related to objective_function_penalty_base" 

This reverts commit 1ad8df4635.
time-shift
Michel Juillard 2015-10-09 14:20:11 +02:00
parent 1a9aa17c9e
commit 035adeb89e
15 changed files with 1084 additions and 1295 deletions
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4
matlab/TaRB_optimizer_wrapper.m
View File

@ -1,4 +1,4 @@
function [fval,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff] = TaRB_optimizer_wrapper(optpar,par_vector,parameterindices,TargetFun,varargin)
function [fval,DLIK,Hess,exit_flag] = TaRB_optimizer_wrapper(optpar,par_vector,parameterindices,TargetFun,varargin)
% function [fval,DLIK,Hess,exit_flag] = TaRB_optimizer_wrapper(optpar,par_vector,parameterindices,TargetFun,varargin)
% Wrapper function for target function used in TaRB algorithm; reassembles
% full parameter vector before calling target function
@ -36,5 +36,5 @@ function [fval,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff] = TaRB_optimize
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
par_vector(parameterindices,:)=optpar; %reassemble parameter
[fval,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff] = feval(TargetFun,par_vector,varargin{:}); %call target function
[fval,DLIK,Hess,exit_flag] = feval(TargetFun,par_vector,varargin{:}); %call target function

735
matlab/dsge_likelihood.m
View File

@ -1,5 +1,7 @@
function [fval,DLIK,Hess,exit_flag,ys,trend_coeff,info,Model,DynareOptions,BayesInfo,DynareResults] = dsge_likelihood(xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults,derivatives_info)
% Evaluates the posterior kernel of a dsge model. Deprecated interface.
function [fval,DLIK,Hess,exit_flag,SteadyState,trend_coeff,info,Model,DynareOptions,BayesInfo,DynareResults] = dsge_likelihood(xparam1,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults,derivatives_info)
% Evaluates the posterior kernel of a dsge model using the specified
% kalman_algo; the resulting posterior includes the 2*pi constant of the
% likelihood function
%@info:
%! @deftypefn {Function File} {[@var{fval},@var{exit_flag},@var{ys},@var{trend_coeff},@var{info},@var{Model},@var{DynareOptions},@var{BayesInfo},@var{DynareResults},@var{DLIK},@var{AHess}] =} dsge_likelihood (@var{xparam1},@var{DynareDataset},@var{DynareOptions},@var{Model},@var{EstimatedParameters},@var{BayesInfo},@var{DynareResults},@var{derivatives_flag})
@ -37,7 +39,7 @@ function [fval,DLIK,Hess,exit_flag,ys,trend_coeff,info,Model,DynareOptions,Bayes
%! Integer scalar, equal to zero if the routine return with a penalty (one otherwise).
%! @item ys
%! Vector of doubles, steady state level for the endogenous variables.
%! @item trend_coeffs
%! @item trend_coeff
%! Matrix of doubles, coefficients of the deterministic trend in the measurement equation.
%! @item info
%! Integer scalar, error code.
@ -68,6 +70,8 @@ function [fval,DLIK,Hess,exit_flag,ys,trend_coeff,info,Model,DynareOptions,Bayes
%! M_.params has been updated in the steadystate routine and has complex valued scalars.
%! @item info==24
%! M_.params has been updated in the steadystate routine and has some NaNs.
%! @item info==26
%! M_.params has been updated in the steadystate routine and has negative/0 values in loglinear model.
%! @item info==30
%! Ergodic variance can't be computed.
%! @item info==41
@ -130,6 +134,725 @@ function [fval,DLIK,Hess,exit_flag,ys,trend_coeff,info,Model,DynareOptions,Bayes
% AUTHOR(S) stephane DOT adjemian AT univ DASH lemans DOT FR
[fval,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff,Model,DynareOptions,BayesInfo,DynareResults] = ...
dsge_likelihood_1(xparam1,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,...
BoundsInfo,DynareResults,derivatives_info);
% Initialization of the returned variables and others...
fval = [];
SteadyState = [];
trend_coeff = [];
exit_flag = 1;
info = 0;
DLIK = [];
Hess = [];
if DynareOptions.estimation_dll
[fval,exit_flag,SteadyState,trend_coeff,info,params,H,Q] ...
= logposterior(xparam1,DynareDataset, DynareOptions,Model, ...
EstimatedParameters,BayesInfo,DynareResults);
mexErrCheck('logposterior', exit_flag);
Model.params = params;
if ~isequal(Model.H,0)
Model.H = H;
end
Model.Sigma_e = Q;
DynareResults.dr.ys = SteadyState;
return
end
% Set flag related to analytical derivatives.
analytic_derivation = DynareOptions.analytic_derivation;
if analytic_derivation && DynareOptions.loglinear
error('The analytic_derivation and loglinear options are not compatible')
end
if nargout==1,
analytic_derivation=0;
end
if analytic_derivation,
kron_flag=DynareOptions.analytic_derivation_mode;
end
%------------------------------------------------------------------------------
% 1. Get the structural parameters & define penalties
%------------------------------------------------------------------------------
% Return, with endogenous penalty, if some parameters are smaller than the lower bound of the prior domain.
if ~isequal(DynareOptions.mode_compute,1) && any(xparam1<BoundsInfo.lb)
k = find(xparam1<BoundsInfo.lb);
fval = Inf;
exit_flag = 0;
info(1) = 41;
info(2) = sum((BoundsInfo.lb(k)-xparam1(k)).^2);
if analytic_derivation,
DLIK=ones(length(xparam1),1);
end
return
end
% Return, with endogenous penalty, if some parameters are greater than the upper bound of the prior domain.
if ~isequal(DynareOptions.mode_compute,1) && any(xparam1>BoundsInfo.ub)
k = find(xparam1>BoundsInfo.ub);
fval = Inf;
exit_flag = 0;
info(1) = 42;
info(2) = sum((xparam1(k)-BoundsInfo.ub(k)).^2);
if analytic_derivation,
DLIK=ones(length(xparam1),1);
end
return
end
% Get the diagonal elements of the covariance matrices for the structural innovations (Q) and the measurement error (H).
Model = set_all_parameters(xparam1,EstimatedParameters,Model);
Q = Model.Sigma_e;
H = Model.H;
% Test if Q is positive definite.
if ~issquare(Q) || EstimatedParameters.ncx || isfield(EstimatedParameters,'calibrated_covariances')
[Q_is_positive_definite, penalty] = ispd(Q);
if ~Q_is_positive_definite
fval = Inf;
exit_flag = 0;
info(1) = 43;
info(2) = penalty;
return
end
if isfield(EstimatedParameters,'calibrated_covariances')
correct_flag=check_consistency_covariances(Q);
if ~correct_flag
fval = Inf;
exit_flag = 0;
info(1) = 71;
info(2) = sum(Q(EstimatedParameters.calibrated_covariances.position).^2);
return
end
end
end
% Test if H is positive definite.
if ~issquare(H) || EstimatedParameters.ncn || isfield(EstimatedParameters,'calibrated_covariances_ME')
[H_is_positive_definite, penalty] = ispd(H);
if ~H_is_positive_definite
fval = Inf;
exit_flag = 0;
info(1) = 44;
info(2) = penalty;
return
end
if isfield(EstimatedParameters,'calibrated_covariances_ME')
correct_flag=check_consistency_covariances(H);
if ~correct_flag
fval = Inf;
exit_flag = 0;
info(1) = 72;
info(2) = sum(H(EstimatedParameters.calibrated_covariances_ME.position).^2);
return
end
end
end
%------------------------------------------------------------------------------
% 2. call model setup & reduction program
%------------------------------------------------------------------------------
% Linearize the model around the deterministic sdteadystate and extract the matrices of the state equation (T and R).
[T,R,SteadyState,info,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults,'restrict');
% Return, with endogenous penalty when possible, if dynare_resolve issues an error code (defined in resol).
if info(1) == 1 || info(1) == 2 || info(1) == 5 || info(1) == 7 || info(1) == 8 || ...
info(1) == 22 || info(1) == 24 || info(1) == 19 || info(1) == 25 || info(1) == 10
fval = Inf;
info(2) = 0.1;
exit_flag = 0;
if analytic_derivation,
DLIK=ones(length(xparam1),1);
end
return
elseif info(1) == 3 || info(1) == 4 || info(1)==6 || info(1) == 20 || info(1) == 21 || info(1) == 23 || info(1)==26
fval = Inf;
exit_flag = 0;
if analytic_derivation,
DLIK=ones(length(xparam1),1);
end
return
end
% check endogenous prior restrictions
info=endogenous_prior_restrictions(T,R,Model,DynareOptions,DynareResults);
if info(1),
fval = Inf;
exit_flag = 0;
if analytic_derivation,
DLIK=ones(length(xparam1),1);
end
return
end
%
% Define a vector of indices for the observed variables. Is this really usefull?...
BayesInfo.mf = BayesInfo.mf1;
% Define the constant vector of the measurement equation.
if DynareOptions.noconstant
constant = zeros(DynareDataset.vobs,1);
else
if DynareOptions.loglinear
constant = log(SteadyState(BayesInfo.mfys));
else
constant = SteadyState(BayesInfo.mfys);
end
end
% Define the deterministic linear trend of the measurement equation.
if BayesInfo.with_trend
trend_coeff = zeros(DynareDataset.vobs,1);
t = DynareOptions.trend_coeffs;
for i=1:length(t)
if ~isempty(t{i})
trend_coeff(i) = evalin('base',t{i});
end
end
trend = repmat(constant,1,DynareDataset.nobs)+trend_coeff*[1:DynareDataset.nobs];
else
trend_coeff = zeros(DynareDataset.vobs,1);
trend = repmat(constant,1,DynareDataset.nobs);
end
% Get needed informations for kalman filter routines.
start = DynareOptions.presample+1;
Z = BayesInfo.mf;
no_missing_data_flag = ~DatasetInfo.missing.state;
mm = length(T);
pp = DynareDataset.vobs;
rr = length(Q);
kalman_tol = DynareOptions.kalman_tol;
diffuse_kalman_tol = DynareOptions.diffuse_kalman_tol;
riccati_tol = DynareOptions.riccati_tol;
Y = transpose(DynareDataset.data)-trend;
%------------------------------------------------------------------------------
% 3. Initial condition of the Kalman filter
%------------------------------------------------------------------------------
kalman_algo = DynareOptions.kalman_algo;
% resetting measurement errors covariance matrix for univariate filters
if (kalman_algo == 2) || (kalman_algo == 4)
if isequal(H,0)
H = zeros(pp,1);
mmm = mm;
else
if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
H = diag(H);
mmm = mm;
else
Z = [Z, eye(pp)];
T = blkdiag(T,zeros(pp));
Q = blkdiag(Q,H);
R = blkdiag(R,eye(pp));
Pstar = blkdiag(Pstar,H);
Pinf = blkdiag(Pinf,zeros(pp));
H = zeros(pp,1);
mmm = mm+pp;
end
end
end
diffuse_periods = 0;
correlated_errors_have_been_checked = 0;
singular_diffuse_filter = 0;
switch DynareOptions.lik_init
case 1% Standard initialization with the steady state of the state equation.
if kalman_algo~=2
% Use standard kalman filter except if the univariate filter is explicitely choosen.
kalman_algo = 1;
end
if DynareOptions.lyapunov_fp == 1
Pstar = lyapunov_symm(T,R*Q'*R',DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold, 3, [], DynareOptions.debug);
elseif DynareOptions.lyapunov_db == 1
Pstar = disclyap_fast(T,R*Q*R',DynareOptions.lyapunov_doubling_tol);
elseif DynareOptions.lyapunov_srs == 1
Pstar = lyapunov_symm(T,Q,DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold, 4, R, DynareOptions.debug);
else
Pstar = lyapunov_symm(T,R*Q*R',DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold, [], [], DynareOptions.debug);
end;
Pinf = [];
a = zeros(mm,1);
Zflag = 0;
case 2% Initialization with large numbers on the diagonal of the covariance matrix if the states (for non stationary models).
if kalman_algo ~= 2
% Use standard kalman filter except if the univariate filter is explicitely choosen.
kalman_algo = 1;
end
Pstar = DynareOptions.Harvey_scale_factor*eye(mm);
Pinf = [];
a = zeros(mm,1);
Zflag = 0;
case 3% Diffuse Kalman filter (Durbin and Koopman)
% Use standard kalman filter except if the univariate filter is explicitely choosen.
if kalman_algo == 0
kalman_algo = 3;
elseif ~((kalman_algo == 3) || (kalman_algo == 4))
error(['The model requires Diffuse filter, but you specified a different Kalman filter. You must set options_.kalman_algo ' ...
'to 0 (default), 3 or 4'])
end
[Z,T,R,QT,Pstar,Pinf] = schur_statespace_transformation(Z,T,R,Q,DynareOptions.qz_criterium);
Zflag = 1;
% Run diffuse kalman filter on first periods.
if (kalman_algo==3)
% Multivariate Diffuse Kalman Filter
if no_missing_data_flag
[dLIK,dlik,a,Pstar] = kalman_filter_d(Y, 1, size(Y,2), ...
zeros(mm,1), Pinf, Pstar, ...
kalman_tol, diffuse_kalman_tol, riccati_tol, DynareOptions.presample, ...
T,R,Q,H,Z,mm,pp,rr);
else
[dLIK,dlik,a,Pstar] = missing_observations_kalman_filter_d(DatasetInfo.missing.aindex,DatasetInfo.missing.number_of_observations,DatasetInfo.missing.no_more_missing_observations, ...
Y, 1, size(Y,2), ...
zeros(mm,1), Pinf, Pstar, ...
kalman_tol, diffuse_kalman_tol, riccati_tol, DynareOptions.presample, ...
T,R,Q,H,Z,mm,pp,rr);
end
diffuse_periods = length(dlik);
if isinf(dLIK)
% Go to univariate diffuse filter if singularity problem.
singular_diffuse_filter = 1;
end
end
if singular_diffuse_filter || (kalman_algo==4)
% Univariate Diffuse Kalman Filter
if isequal(H,0)
H1 = zeros(pp,1);
mmm = mm;
else
if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
H1 = diag(H);
mmm = mm;
else
Z = [Z, eye(pp)];
T = blkdiag(T,zeros(pp));
Q = blkdiag(Q,H);
R = blkdiag(R,eye(pp));
Pstar = blkdiag(Pstar,H);
Pinf = blkdiag(Pinf,zeros(pp));
H1 = zeros(pp,1);
mmm = mm+pp;
end
end
% no need to test again for correlation elements
correlated_errors_have_been_checked = 1;
[dLIK,dlik,a,Pstar] = univariate_kalman_filter_d(DatasetInfo.missing.aindex,...
DatasetInfo.missing.number_of_observations,...
DatasetInfo.missing.no_more_missing_observations, ...
Y, 1, size(Y,2), ...
zeros(mmm,1), Pinf, Pstar, ...
kalman_tol, diffuse_kalman_tol, riccati_tol, DynareOptions.presample, ...
T,R,Q,H1,Z,mmm,pp,rr);
diffuse_periods = size(dlik,1);
end
if isnan(dLIK),
fval = Inf;
info(1) = 45;
info(2) = 0.1;
exit_flag = 0;
return
end
case 4% Start from the solution of the Riccati equation.
if kalman_algo ~= 2
kalman_algo = 1;
end
if isequal(H,0)
[err,Pstar] = kalman_steady_state(transpose(T),R*Q*transpose(R),transpose(build_selection_matrix(Z,mm,length(Z))));
else
[err,Pstar] = kalman_steady_state(transpose(T),R*Q*transpose(R),transpose(build_selection_matrix(Z,mm,length(Z))),H);
end
if err
disp(['dsge_likelihood:: I am not able to solve the Riccati equation, so I switch to lik_init=1!']);
DynareOptions.lik_init = 1;
Pstar = lyapunov_symm(T,R*Q*R',DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold, [], [], DynareOptions.debug);
end
Pinf = [];
a = zeros(mm,1);
Zflag = 0;
case 5 % Old diffuse Kalman filter only for the non stationary variables
[eigenvect, eigenv] = eig(T);
eigenv = diag(eigenv);
nstable = length(find(abs(abs(eigenv)-1) > 1e-7));
unstable = find(abs(abs(eigenv)-1) < 1e-7);
V = eigenvect(:,unstable);
indx_unstable = find(sum(abs(V),2)>1e-5);
stable = find(sum(abs(V),2)<1e-5);
nunit = length(eigenv) - nstable;
Pstar = options_.Harvey_scale_factor*eye(np);
if kalman_algo ~= 2
kalman_algo = 1;
end
R_tmp = R(stable, :);
T_tmp = T(stable,stable);
if DynareOptions.lyapunov_fp == 1
Pstar_tmp = lyapunov_symm(T_tmp,R_tmp*Q*R_tmp',DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold, 3, [], DynareOptions.debug);
elseif DynareOptions.lyapunov_db == 1
Pstar_tmp = disclyap_fast(T_tmp,R_tmp*Q*R_tmp',DynareOptions.lyapunov_doubling_tol);
elseif DynareOptions.lyapunov_srs == 1
Pstar_tmp = lyapunov_symm(T_tmp,Q,DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold, 4, R_tmp, DynareOptions.debug);
else
Pstar_tmp = lyapunov_symm(T_tmp,R_tmp*Q*R_tmp',DynareOptions.qz_criterium,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold, [], [], DynareOptions.debug);
end
Pstar(stable, stable) = Pstar_tmp;
Pinf = [];
otherwise
error('dsge_likelihood:: Unknown initialization approach for the Kalman filter!')
end
if analytic_derivation,
offset = EstimatedParameters.nvx;
offset = offset+EstimatedParameters.nvn;
offset = offset+EstimatedParameters.ncx;
offset = offset+EstimatedParameters.ncn;
no_DLIK = 0;
full_Hess = analytic_derivation==2;
asy_Hess = analytic_derivation==-2;
outer_product_gradient = analytic_derivation==-1;
if asy_Hess,
analytic_derivation=1;
end
if outer_product_gradient,
analytic_derivation=1;
end
DLIK = [];
AHess = [];
iv = DynareResults.dr.restrict_var_list;
if nargin<10 || isempty(derivatives_info)
[A,B,nou,nou,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults);
if ~isempty(EstimatedParameters.var_exo)
indexo=EstimatedParameters.var_exo(:,1);
else
indexo=[];
end
if ~isempty(EstimatedParameters.param_vals)
indparam=EstimatedParameters.param_vals(:,1);
else
indparam=[];
end
if full_Hess,
[dum, DT, DOm, DYss, dum2, D2T, D2Om, D2Yss] = getH(A, B, Model,DynareResults,DynareOptions,kron_flag,indparam,indexo,iv);
clear dum dum2;
else
[dum, DT, DOm, DYss] = getH(A, B, Model,DynareResults,DynareOptions,kron_flag,indparam,indexo,iv);
end
else
DT = derivatives_info.DT(iv,iv,:);
DOm = derivatives_info.DOm(iv,iv,:);
DYss = derivatives_info.DYss(iv,:);
if isfield(derivatives_info,'full_Hess'),
full_Hess = derivatives_info.full_Hess;
end
if full_Hess,
D2T = derivatives_info.D2T;
D2Om = derivatives_info.D2Om;
D2Yss = derivatives_info.D2Yss;
end
if isfield(derivatives_info,'no_DLIK'),
no_DLIK = derivatives_info.no_DLIK;
end
clear('derivatives_info');
end
DYss = [zeros(size(DYss,1),offset) DYss];
DH=zeros([length(H),length(H),length(xparam1)]);
DQ=zeros([size(Q),length(xparam1)]);
DP=zeros([size(T),length(xparam1)]);
if full_Hess,
for j=1:size(D2Yss,1),
tmp(j,:,:) = blkdiag(zeros(offset,offset), squeeze(D2Yss(j,:,:)));
end
D2Yss = tmp;
D2H=sparse(size(D2Om,1),size(D2Om,2)); %zeros([size(H),length(xparam1),length(xparam1)]);
D2P=sparse(size(D2Om,1),size(D2Om,2)); %zeros([size(T),length(xparam1),length(xparam1)]);
jcount=0;
end
if DynareOptions.lik_init==1,
for i=1:EstimatedParameters.nvx
k =EstimatedParameters.var_exo(i,1);
DQ(k,k,i) = 2*sqrt(Q(k,k));
dum = lyapunov_symm(T,DOm(:,:,i),DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold,[],[],DynareOptions.debug);
% kk = find(abs(dum) < 1e-12);
% dum(kk) = 0;
DP(:,:,i)=dum;
if full_Hess
for j=1:i,
jcount=jcount+1;
dum = lyapunov_symm(T,dyn_unvech(D2Om(:,jcount)),DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold,[],[],DynareOptions.debug);
% kk = (abs(dum) < 1e-12);
% dum(kk) = 0;
D2P(:,jcount)=dyn_vech(dum);
% D2P(:,:,j,i)=dum;
end
end
end
end
offset = EstimatedParameters.nvx;
for i=1:EstimatedParameters.nvn
k = EstimatedParameters.var_endo(i,1);
DH(k,k,i+offset) = 2*sqrt(H(k,k));
if full_Hess
D2H(k,k,i+offset,i+offset) = 2;
end
end
offset = offset + EstimatedParameters.nvn;
if DynareOptions.lik_init==1,
for j=1:EstimatedParameters.np
dum = lyapunov_symm(T,DT(:,:,j+offset)*Pstar*T'+T*Pstar*DT(:,:,j+offset)'+DOm(:,:,j+offset),DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold,[],[],DynareOptions.debug);
% kk = find(abs(dum) < 1e-12);
% dum(kk) = 0;
DP(:,:,j+offset)=dum;
if full_Hess
DTj = DT(:,:,j+offset);
DPj = dum;
for i=1:j+offset,
jcount=jcount+1;
DTi = DT(:,:,i);
DPi = DP(:,:,i);
D2Tij = reshape(D2T(:,jcount),size(T));
D2Omij = dyn_unvech(D2Om(:,jcount));
tmp = D2Tij*Pstar*T' + T*Pstar*D2Tij' + DTi*DPj*T' + DTj*DPi*T' + T*DPj*DTi' + T*DPi*DTj' + DTi*Pstar*DTj' + DTj*Pstar*DTi' + D2Omij;
dum = lyapunov_symm(T,tmp,DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold,[],[],DynareOptions.debug);
% dum(abs(dum)<1.e-12) = 0;
D2P(:,jcount) = dyn_vech(dum);
% D2P(:,:,j+offset,i) = dum;
end
end
end
end
if analytic_derivation==1,
analytic_deriv_info={analytic_derivation,DT,DYss,DOm,DH,DP,asy_Hess};
else
analytic_deriv_info={analytic_derivation,DT,DYss,DOm,DH,DP,D2T,D2Yss,D2Om,D2H,D2P};
clear DT DYss DOm DP D2T D2Yss D2Om D2H D2P,
end
else
analytic_deriv_info={0};
end
%------------------------------------------------------------------------------
% 4. Likelihood evaluation
%------------------------------------------------------------------------------
if ((kalman_algo==1) || (kalman_algo==3))% Multivariate Kalman Filter
if no_missing_data_flag
if DynareOptions.block
[err, LIK] = block_kalman_filter(T,R,Q,H,Pstar,Y,start,Z,kalman_tol,riccati_tol, Model.nz_state_var, Model.n_diag, Model.nobs_non_statevar);
mexErrCheck('block_kalman_filter', err);
else
[LIK,lik] = kalman_filter(Y,diffuse_periods+1,size(Y,2), ...
a,Pstar, ...
kalman_tol, riccati_tol, ...
DynareOptions.presample, ...
T,Q,R,H,Z,mm,pp,rr,Zflag,diffuse_periods, ...
analytic_deriv_info{:});
end
else
if 0 %DynareOptions.block
[err, LIK,lik] = block_kalman_filter(DatasetInfo.missing.aindex,DatasetInfo.missing.number_of_observations,DatasetInfo.missing.no_more_missing_observations,...
T,R,Q,H,Pstar,Y,start,Z,kalman_tol,riccati_tol, Model.nz_state_var, Model.n_diag, Model.nobs_non_statevar);
else
[LIK,lik] = missing_observations_kalman_filter(DatasetInfo.missing.aindex,DatasetInfo.missing.number_of_observations,DatasetInfo.missing.no_more_missing_observations,Y,diffuse_periods+1,size(Y,2), ...
a, Pstar, ...
kalman_tol, DynareOptions.riccati_tol, ...
DynareOptions.presample, ...
T,Q,R,H,Z,mm,pp,rr,Zflag,diffuse_periods);
end
end
if analytic_derivation,
LIK1=LIK;
LIK=LIK1{1};
lik1=lik;
lik=lik1{1};
end
if isinf(LIK)
if DynareOptions.use_univariate_filters_if_singularity_is_detected
if kalman_algo == 1
kalman_algo = 2;
else
kalman_algo = 4;
end
else
if isinf(LIK)
fval = Inf;
info(1) = 66;
info(2) = 0.1;
exit_flag = 0;
return
end
end
else
if DynareOptions.lik_init==3
LIK = LIK + dLIK;
if analytic_derivation==0 && nargout==2,
lik = [dlik; lik];
end
end
end
end
if (kalman_algo==2) || (kalman_algo==4)
% Univariate Kalman Filter
% resetting measurement error covariance matrix when necessary %
if ~correlated_errors_have_been_checked
if isequal(H,0)
H1 = zeros(pp,1);
mmm = mm;
if analytic_derivation,
DH = zeros(pp,length(xparam1));
end
else
if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
H1 = diag(H);
mmm = mm;
clear tmp
if analytic_derivation,
for j=1:pp,
tmp(j,:)=DH(j,j,:);
end
DH=tmp;
end
else
Z = [Z, eye(pp)];
T = blkdiag(T,zeros(pp));
Q = blkdiag(Q,H);
R = blkdiag(R,eye(pp));
Pstar = blkdiag(Pstar,H);
Pinf = blkdiag(Pinf,zeros(pp));
H1 = zeros(pp,1);
mmm = mm+pp;
end
end
if analytic_derivation,
analytic_deriv_info{5}=DH;
end
end
[LIK, lik] = univariate_kalman_filter(DatasetInfo.missing.aindex,DatasetInfo.missing.number_of_observations,DatasetInfo.missing.no_more_missing_observations,Y,diffuse_periods+1,size(Y,2), ...
a,Pstar, ...
DynareOptions.kalman_tol, ...
DynareOptions.riccati_tol, ...
DynareOptions.presample, ...
T,Q,R,H1,Z,mmm,pp,rr,Zflag,diffuse_periods,analytic_deriv_info{:});
if analytic_derivation,
LIK1=LIK;
LIK=LIK1{1};
lik1=lik;
lik=lik1{1};
end
if DynareOptions.lik_init==3
LIK = LIK+dLIK;
if analytic_derivation==0 && nargout==2,
lik = [dlik; lik];
end
end
end
if analytic_derivation
if no_DLIK==0
DLIK = LIK1{2};
% [DLIK] = score(T,R,Q,H,Pstar,Y,DT,DYss,DOm,DH,DP,start,Z,kalman_tol,riccati_tol);
end
if full_Hess ,
Hess = -LIK1{3};
% [Hess, DLL] = get_Hessian(T,R,Q,H,Pstar,Y,DT,DYss,DOm,DH,DP,D2T,D2Yss,D2Om,D2H,D2P,start,Z,kalman_tol,riccati_tol);
% Hess0 = getHessian(Y,T,DT,D2T, R*Q*transpose(R),DOm,D2Om,Z,DYss,D2Yss);
end
if asy_Hess,
% if ~((kalman_algo==2) || (kalman_algo==4)),
% [Hess] = AHessian(T,R,Q,H,Pstar,Y,DT,DYss,DOm,DH,DP,start,Z,kalman_tol,riccati_tol);
% else
Hess = LIK1{3};
% end
end
end
if isnan(LIK)
fval = Inf;
info(1) = 45;
info(2) = 0.1;
exit_flag = 0;
return
end
if imag(LIK)~=0
fval = Inf;
info(1) = 46;
info(2) = 0.1;
exit_flag = 0;
return
end
likelihood = LIK;
% ------------------------------------------------------------------------------
% 5. Adds prior if necessary
% ------------------------------------------------------------------------------
if analytic_derivation
if full_Hess,
[lnprior, dlnprior, d2lnprior] = priordens(xparam1,BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4);
Hess = Hess - d2lnprior;
else
[lnprior, dlnprior] = priordens(xparam1,BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4);
end
if no_DLIK==0
DLIK = DLIK - dlnprior';
end
if outer_product_gradient,
dlik = lik1{2};
dlik=[- dlnprior; dlik(start:end,:)];
Hess = dlik'*dlik;
end
else
lnprior = priordens(xparam1,BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4);
end
if DynareOptions.endogenous_prior==1
if DynareOptions.lik_init==2 || DynareOptions.lik_init==3
error('Endogenous prior not supported with non-stationary models')
else
[lnpriormom] = endogenous_prior(Y,Pstar,BayesInfo,H);
fval = (likelihood-lnprior-lnpriormom);
end
else
fval = (likelihood-lnprior);
end
if DynareOptions.prior_restrictions.status
tmp = feval(DynareOptions.prior_restrictions.routine, Model, DynareResults, DynareOptions, DynareDataset, DatasetInfo);
fval = fval - tmp;
end
if isnan(fval)
fval = Inf;
info(1) = 47;
info(2) = 0.1;
exit_flag = 0;
return
end
if imag(fval)~=0
fval = Inf;
info(1) = 48;
info(2) = 0.1;
exit_flag = 0;
return
end
% Update DynareOptions.kalman_algo.
DynareOptions.kalman_algo = kalman_algo;
if analytic_derivation==0 && nargout > 1
lik=lik(start:end,:);
DLIK=[-lnprior; lik(:)];
end

858
matlab/dsge_likelihood_1.m
View File

@ -1,858 +0,0 @@
function [fval,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff,Model,DynareOptions,BayesInfo,DynareResults] = dsge_likelihood_1(xparam1,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults,derivatives_info)
% Evaluates the posterior kernel of a dsge model using the specified
% kalman_algo; the resulting posterior includes the 2*pi constant of the
% likelihood function.
%@info:
%! @deftypefn {Function File} {[@var{fval},@var{exit_flag},@var{ys},@var{trend_coeff},@var{info},@var{Model},@var{DynareOptions},@var{BayesInfo},@var{DynareResults},@var{DLIK},@var{AHess}] =} dsge_likelihood (@var{xparam1},@var{DynareDataset},@var{DynareOptions},@var{Model},@var{EstimatedParameters},@var{BayesInfo},@var{DynareResults},@var{derivatives_flag})
%! @anchor{dsge_likelihood}
%! @sp 1
%! Evaluates the posterior kernel of a dsge model.
%! @sp 2
%! @strong{Inputs}
%! @sp 1
%! @table @ @var
%! @item xparam1
%! Vector of doubles, current values for the estimated parameters.
%! @item DynareDataset
%! Matlab's structure describing the dataset (initialized by dynare, see @ref{dataset_}).
%! @item DynareOptions
%! Matlab's structure describing the options (initialized by dynare, see @ref{options_}).
%! @item Model
%! Matlab's structure describing the Model (initialized by dynare, see @ref{M_}).
%! @item EstimatedParamemeters
%! Matlab's structure describing the estimated_parameters (initialized by dynare, see @ref{estim_params_}).
%! @item BayesInfo
%! Matlab's structure describing the priors (initialized by dynare, see @ref{bayesopt_}).
%! @item DynareResults
%! Matlab's structure gathering the results (initialized by dynare, see @ref{oo_}).
%! @item derivates_flag
%! Integer scalar, flag for analytical derivatives of the likelihood.
%! @end table
%! @sp 2
%! @strong{Outputs}
%! @sp 1
%! @table @ @var
%! @item fval
%! Double scalar, value of (minus) the likelihood.
%! @item info
%! Integer scalar, error code.
%! @table @ @code
%! @item info==0
%! No error.
%! @item info==1
%! The model doesn't determine the current variables uniquely.
%! @item info==2
%! MJDGGES returned an error code.
%! @item info==3
%! Blanchard & Kahn conditions are not satisfied: no stable equilibrium.
%! @item info==4
%! Blanchard & Kahn conditions are not satisfied: indeterminacy.
%! @item info==5
%! Blanchard & Kahn conditions are not satisfied: indeterminacy due to rank failure.
%! @item info==6
%! The jacobian evaluated at the deterministic steady state is complex.
%! @item info==19
%! The steadystate routine thrown an exception (inconsistent deep parameters).
%! @item info==20
%! Cannot find the steady state, info(2) contains the sum of square residuals (of the static equations).
%! @item info==21
%! The steady state is complex, info(2) contains the sum of square of imaginary parts of the steady state.
%! @item info==22
%! The steady has NaNs.
%! @item info==23
%! M_.params has been updated in the steadystate routine and has complex valued scalars.
%! @item info==24
%! M_.params has been updated in the steadystate routine and has some NaNs.
%! @item info==26
%! M_.params has been updated in the steadystate routine and has negative/0 values in loglinear model.
%! @item info==30
%! Ergodic variance can't be computed.
%! @item info==41
%! At least one parameter is violating a lower bound condition.
%! @item info==42
%! At least one parameter is violating an upper bound condition.
%! @item info==43
%! The covariance matrix of the structural innovations is not positive definite.
%! @item info==44
%! The covariance matrix of the measurement errors is not positive definite.
%! @item info==45
%! Likelihood is not a number (NaN).
%! @item info==46
%! Likelihood is a complex valued number.
%! @item info==47
%! Posterior kernel is not a number (logged prior density is NaN)
%! @item info==48
%! Posterior kernel is a complex valued number (logged prior density is complex).
%! @end table
%! @item exit_flag
%! Integer scalar, equal to zero if the routine return with a penalty (one otherwise).
%! @item ys
%! Vector of doubles, steady state level for the endogenous variables.
%! @item trend_coeff
%! Matrix of doubles, coefficients of the deterministic trend in the measurement equation.
%! @item Model
%! Matlab's structure describing the model (initialized by dynare, see @ref{M_}).
%! @item DynareOptions
%! Matlab's structure describing the options (initialized by dynare, see @ref{options_}).
%! @item BayesInfo
%! Matlab's structure describing the priors (initialized by dynare, see @ref{bayesopt_}).
%! @item DynareResults
%! Matlab's structure gathering the results (initialized by dynare, see @ref{oo_}).
%! @item DLIK
%! Vector of doubles, score of the likelihood.
%! @item AHess
%! Matrix of doubles, asymptotic hessian matrix.
%! @end table
%! @sp 2
%! @strong{This function is called by:}
%! @sp 1
%! @ref{dynare_estimation_1}, @ref{mode_check}
%! @sp 2
%! @strong{This function calls:}
%! @sp 1
%! @ref{dynare_resolve}, @ref{lyapunov_symm}, @ref{schur_statespace_transformation}, @ref{kalman_filter_d}, @ref{missing_observations_kalman_filter_d}, @ref{univariate_kalman_filter_d}, @ref{kalman_steady_state}, @ref{getH}, @ref{kalman_filter}, @ref{score}, @ref{AHessian}, @ref{missing_observations_kalman_filter}, @ref{univariate_kalman_filter}, @ref{priordens}
%! @end deftypefn
%@eod:
% Copyright (C) 2004-2013 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
% AUTHOR(S) stephane DOT adjemian AT univ DASH lemans DOT FR
% Initialization of the returned variables and others...
fval = [];
SteadyState = [];
trend_coeff = [];
exit_flag = 1;
info = 0;
DLIK = [];
Hess = [];
if DynareOptions.estimation_dll
[fval,exit_flag,SteadyState,trend_coeff,info,params,H,Q] ...
= logposterior(xparam1,DynareDataset, DynareOptions,Model, ...
EstimatedParameters,BayesInfo,DynareResults);
mexErrCheck('logposterior', exit_flag);
Model.params = params;
if ~isequal(Model.H,0)
Model.H = H;
end
Model.Sigma_e = Q;
DynareResults.dr.ys = SteadyState;
return
end
% Set flag related to analytical derivatives.
analytic_derivation = DynareOptions.analytic_derivation;
if analytic_derivation && DynareOptions.loglinear
error('The analytic_derivation and loglinear options are not compatible')
end
if nargout==1,
analytic_derivation=0;
end
if analytic_derivation,
kron_flag=DynareOptions.analytic_derivation_mode;
end
%------------------------------------------------------------------------------
% 1. Get the structural parameters & define penalties
%------------------------------------------------------------------------------
% Return, with endogenous penalty, if some parameters are smaller than the lower bound of the prior domain.
if ~isequal(DynareOptions.mode_compute,1) && any(xparam1<BoundsInfo.lb)
k = find(xparam1<BoundsInfo.lb);
fval = Inf;
exit_flag = 0;
info(1) = 41;
info(2) = sum((BoundsInfo.lb(k)-xparam1(k)).^2);
if analytic_derivation,
DLIK=ones(length(xparam1),1);
end
return
end
% Return, with endogenous penalty, if some parameters are greater than the upper bound of the prior domain.
if ~isequal(DynareOptions.mode_compute,1) && any(xparam1>BoundsInfo.ub)
k = find(xparam1>BoundsInfo.ub);
fval = Inf;
exit_flag = 0;
info(1) = 42;
info(2) = sum((xparam1(k)-BoundsInfo.ub(k)).^2);
if analytic_derivation,
DLIK=ones(length(xparam1),1);
end
return
end
% Get the diagonal elements of the covariance matrices for the structural innovations (Q) and the measurement error (H).
Model = set_all_parameters(xparam1,EstimatedParameters,Model);
Q = Model.Sigma_e;
H = Model.H;
% Test if Q is positive definite.
if ~issquare(Q) || EstimatedParameters.ncx || isfield(EstimatedParameters,'calibrated_covariances')
[Q_is_positive_definite, penalty] = ispd(Q);
if ~Q_is_positive_definite
fval = Inf;
exit_flag = 0;
info(1) = 43;
info(2) = penalty;
return
end
if isfield(EstimatedParameters,'calibrated_covariances')
correct_flag=check_consistency_covariances(Q);
if ~correct_flag
fval = Inf;
exit_flag = 0;
info(1) = 71;
info(2) = sum(Q(EstimatedParameters.calibrated_covariances.position).^2);
return
end
end
end
% Test if H is positive definite.
if ~issquare(H) || EstimatedParameters.ncn || isfield(EstimatedParameters,'calibrated_covariances_ME')
[H_is_positive_definite, penalty] = ispd(H);
if ~H_is_positive_definite
fval = Inf;
exit_flag = 0;
info(1) = 44;
info(2) = penalty;
return
end
if isfield(EstimatedParameters,'calibrated_covariances_ME')
correct_flag=check_consistency_covariances(H);
if ~correct_flag
fval = Inf;
exit_flag = 0;
info(1) = 72;
info(2) = sum(H(EstimatedParameters.calibrated_covariances_ME.position).^2);
return
end
end
end
%------------------------------------------------------------------------------
% 2. call model setup & reduction program
%------------------------------------------------------------------------------
% Linearize the model around the deterministic sdteadystate and extract the matrices of the state equation (T and R).
[T,R,SteadyState,info,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults,'restrict');
% Return, with endogenous penalty when possible, if dynare_resolve issues an error code (defined in resol).
if info(1) == 1 || info(1) == 2 || info(1) == 5 || info(1) == 7 || info(1) == 8 || ...
info(1) == 22 || info(1) == 24 || info(1) == 19 || info(1) == 25 || info(1) == 10
fval = Inf;
info(2) = 0.1;
exit_flag = 0;
if analytic_derivation,
DLIK=ones(length(xparam1),1);
end
return
elseif info(1) == 3 || info(1) == 4 || info(1)==6 || info(1) == 20 || info(1) == 21 || info(1) == 23 || info(1)==26
fval = Inf;
exit_flag = 0;
if analytic_derivation,
DLIK=ones(length(xparam1),1);
end
return
end
% check endogenous prior restrictions
info=endogenous_prior_restrictions(T,R,Model,DynareOptions,DynareResults);
if info(1),
fval = Inf;
exit_flag = 0;
if analytic_derivation,
DLIK=ones(length(xparam1),1);
end
return
end
%
% Define a vector of indices for the observed variables. Is this really usefull?...
BayesInfo.mf = BayesInfo.mf1;
% Define the constant vector of the measurement equation.
if DynareOptions.noconstant
constant = zeros(DynareDataset.vobs,1);
else
if DynareOptions.loglinear
constant = log(SteadyState(BayesInfo.mfys));
else
constant = SteadyState(BayesInfo.mfys);
end
end
% Define the deterministic linear trend of the measurement equation.
if BayesInfo.with_trend
trend_coeff = zeros(DynareDataset.vobs,1);
t = DynareOptions.trend_coeffs;
for i=1:length(t)
if ~isempty(t{i})
trend_coeff(i) = evalin('base',t{i});
end
end
trend = repmat(constant,1,DynareDataset.nobs)+trend_coeff*[1:DynareDataset.nobs];
else
trend_coeff = zeros(DynareDataset.vobs,1);
trend = repmat(constant,1,DynareDataset.nobs);
end
% Get needed informations for kalman filter routines.
start = DynareOptions.presample+1;
Z = BayesInfo.mf;
no_missing_data_flag = ~DatasetInfo.missing.state;
mm = length(T);
pp = DynareDataset.vobs;
rr = length(Q);
kalman_tol = DynareOptions.kalman_tol;
diffuse_kalman_tol = DynareOptions.diffuse_kalman_tol;
riccati_tol = DynareOptions.riccati_tol;
Y = transpose(DynareDataset.data)-trend;
%------------------------------------------------------------------------------
% 3. Initial condition of the Kalman filter
%------------------------------------------------------------------------------
kalman_algo = DynareOptions.kalman_algo;
% resetting measurement errors covariance matrix for univariate filters
if (kalman_algo == 2) || (kalman_algo == 4)
if isequal(H,0)
H = zeros(pp,1);
mmm = mm;
else
if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
H = diag(H);
mmm = mm;
else
Z = [Z, eye(pp)];
T = blkdiag(T,zeros(pp));
Q = blkdiag(Q,H);
R = blkdiag(R,eye(pp));
Pstar = blkdiag(Pstar,H);
Pinf = blkdiag(Pinf,zeros(pp));
H = zeros(pp,1);
mmm = mm+pp;
end
end
end
diffuse_periods = 0;
correlated_errors_have_been_checked = 0;
singular_diffuse_filter = 0;
switch DynareOptions.lik_init
case 1% Standard initialization with the steady state of the state equation.
if kalman_algo~=2
% Use standard kalman filter except if the univariate filter is explicitely choosen.
kalman_algo = 1;
end
if DynareOptions.lyapunov_fp == 1
Pstar = lyapunov_symm(T,R*Q'*R',DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold, 3, [], DynareOptions.debug);
elseif DynareOptions.lyapunov_db == 1
Pstar = disclyap_fast(T,R*Q*R',DynareOptions.lyapunov_doubling_tol);
elseif DynareOptions.lyapunov_srs == 1
Pstar = lyapunov_symm(T,Q,DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold, 4, R, DynareOptions.debug);
else
Pstar = lyapunov_symm(T,R*Q*R',DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold, [], [], DynareOptions.debug);
end;
Pinf = [];
a = zeros(mm,1);
Zflag = 0;
case 2% Initialization with large numbers on the diagonal of the covariance matrix if the states (for non stationary models).
if kalman_algo ~= 2
% Use standard kalman filter except if the univariate filter is explicitely choosen.
kalman_algo = 1;
end
Pstar = DynareOptions.Harvey_scale_factor*eye(mm);
Pinf = [];
a = zeros(mm,1);
Zflag = 0;
case 3% Diffuse Kalman filter (Durbin and Koopman)
% Use standard kalman filter except if the univariate filter is explicitely choosen.
if kalman_algo == 0
kalman_algo = 3;
elseif ~((kalman_algo == 3) || (kalman_algo == 4))
error(['The model requires Diffuse filter, but you specified a different Kalman filter. You must set options_.kalman_algo ' ...
'to 0 (default), 3 or 4'])
end
[Z,T,R,QT,Pstar,Pinf] = schur_statespace_transformation(Z,T,R,Q,DynareOptions.qz_criterium);
Zflag = 1;
% Run diffuse kalman filter on first periods.
if (kalman_algo==3)
% Multivariate Diffuse Kalman Filter
if no_missing_data_flag
[dLIK,dlik,a,Pstar] = kalman_filter_d(Y, 1, size(Y,2), ...
zeros(mm,1), Pinf, Pstar, ...
kalman_tol, diffuse_kalman_tol, riccati_tol, DynareOptions.presample, ...
T,R,Q,H,Z,mm,pp,rr);
else
[dLIK,dlik,a,Pstar] = missing_observations_kalman_filter_d(DatasetInfo.missing.aindex,DatasetInfo.missing.number_of_observations,DatasetInfo.missing.no_more_missing_observations, ...
Y, 1, size(Y,2), ...
zeros(mm,1), Pinf, Pstar, ...
kalman_tol, diffuse_kalman_tol, riccati_tol, DynareOptions.presample, ...
T,R,Q,H,Z,mm,pp,rr);
end
diffuse_periods = length(dlik);
if isinf(dLIK)
% Go to univariate diffuse filter if singularity problem.
singular_diffuse_filter = 1;
end
end
if singular_diffuse_filter || (kalman_algo==4)
% Univariate Diffuse Kalman Filter
if isequal(H,0)
H1 = zeros(pp,1);
mmm = mm;
else
if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
H1 = diag(H);
mmm = mm;
else
Z = [Z, eye(pp)];
T = blkdiag(T,zeros(pp));
Q = blkdiag(Q,H);
R = blkdiag(R,eye(pp));
Pstar = blkdiag(Pstar,H);
Pinf = blkdiag(Pinf,zeros(pp));
H1 = zeros(pp,1);
mmm = mm+pp;
end
end
% no need to test again for correlation elements
correlated_errors_have_been_checked = 1;
[dLIK,dlik,a,Pstar] = univariate_kalman_filter_d(DatasetInfo.missing.aindex,...
DatasetInfo.missing.number_of_observations,...
DatasetInfo.missing.no_more_missing_observations, ...
Y, 1, size(Y,2), ...
zeros(mmm,1), Pinf, Pstar, ...
kalman_tol, diffuse_kalman_tol, riccati_tol, DynareOptions.presample, ...
T,R,Q,H1,Z,mmm,pp,rr);
diffuse_periods = size(dlik,1);
end
if isnan(dLIK),
fval = Inf;
info(1) = 45;
info(2) = 0.1;
exit_flag = 0;
return
end
case 4% Start from the solution of the Riccati equation.
if kalman_algo ~= 2
kalman_algo = 1;
end
if isequal(H,0)
[err,Pstar] = kalman_steady_state(transpose(T),R*Q*transpose(R),transpose(build_selection_matrix(Z,mm,length(Z))));
else
[err,Pstar] = kalman_steady_state(transpose(T),R*Q*transpose(R),transpose(build_selection_matrix(Z,mm,length(Z))),H);
end
if err
disp(['dsge_likelihood:: I am not able to solve the Riccati equation, so I switch to lik_init=1!']);
DynareOptions.lik_init = 1;
Pstar = lyapunov_symm(T,R*Q*R',DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold, [], [], DynareOptions.debug);
end
Pinf = [];
a = zeros(mm,1);
Zflag = 0;
case 5 % Old diffuse Kalman filter only for the non stationary variables
[eigenvect, eigenv] = eig(T);
eigenv = diag(eigenv);
nstable = length(find(abs(abs(eigenv)-1) > 1e-7));
unstable = find(abs(abs(eigenv)-1) < 1e-7);
V = eigenvect(:,unstable);
indx_unstable = find(sum(abs(V),2)>1e-5);
stable = find(sum(abs(V),2)<1e-5);
nunit = length(eigenv) - nstable;
Pstar = options_.Harvey_scale_factor*eye(np);
if kalman_algo ~= 2
kalman_algo = 1;
end
R_tmp = R(stable, :);
T_tmp = T(stable,stable);
if DynareOptions.lyapunov_fp == 1
Pstar_tmp = lyapunov_symm(T_tmp,R_tmp*Q*R_tmp',DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold, 3, [], DynareOptions.debug);
elseif DynareOptions.lyapunov_db == 1
Pstar_tmp = disclyap_fast(T_tmp,R_tmp*Q*R_tmp',DynareOptions.lyapunov_doubling_tol);
elseif DynareOptions.lyapunov_srs == 1
Pstar_tmp = lyapunov_symm(T_tmp,Q,DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold, 4, R_tmp, DynareOptions.debug);
else
Pstar_tmp = lyapunov_symm(T_tmp,R_tmp*Q*R_tmp',DynareOptions.qz_criterium,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold, [], [], DynareOptions.debug);
end
Pstar(stable, stable) = Pstar_tmp;
Pinf = [];
otherwise
error('dsge_likelihood:: Unknown initialization approach for the Kalman filter!')
end
if analytic_derivation,
offset = EstimatedParameters.nvx;
offset = offset+EstimatedParameters.nvn;
offset = offset+EstimatedParameters.ncx;
offset = offset+EstimatedParameters.ncn;
no_DLIK = 0;
full_Hess = analytic_derivation==2;
asy_Hess = analytic_derivation==-2;
outer_product_gradient = analytic_derivation==-1;
if asy_Hess,
analytic_derivation=1;
end
if outer_product_gradient,
analytic_derivation=1;
end
DLIK = [];
AHess = [];
iv = DynareResults.dr.restrict_var_list;
if nargin<10 || isempty(derivatives_info)
[A,B,nou,nou,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults);
if ~isempty(EstimatedParameters.var_exo)
indexo=EstimatedParameters.var_exo(:,1);
else
indexo=[];
end
if ~isempty(EstimatedParameters.param_vals)
indparam=EstimatedParameters.param_vals(:,1);
else
indparam=[];
end
if full_Hess,
[dum, DT, DOm, DYss, dum2, D2T, D2Om, D2Yss] = getH(A, B, Model,DynareResults,DynareOptions,kron_flag,indparam,indexo,iv);
clear dum dum2;
else
[dum, DT, DOm, DYss] = getH(A, B, Model,DynareResults,DynareOptions,kron_flag,indparam,indexo,iv);
end
else
DT = derivatives_info.DT(iv,iv,:);
DOm = derivatives_info.DOm(iv,iv,:);
DYss = derivatives_info.DYss(iv,:);
if isfield(derivatives_info,'full_Hess'),
full_Hess = derivatives_info.full_Hess;
end
if full_Hess,
D2T = derivatives_info.D2T;
D2Om = derivatives_info.D2Om;
D2Yss = derivatives_info.D2Yss;
end
if isfield(derivatives_info,'no_DLIK'),
no_DLIK = derivatives_info.no_DLIK;
end
clear('derivatives_info');
end
DYss = [zeros(size(DYss,1),offset) DYss];
DH=zeros([length(H),length(H),length(xparam1)]);
DQ=zeros([size(Q),length(xparam1)]);
DP=zeros([size(T),length(xparam1)]);
if full_Hess,
for j=1:size(D2Yss,1),
tmp(j,:,:) = blkdiag(zeros(offset,offset), squeeze(D2Yss(j,:,:)));
end
D2Yss = tmp;
D2H=sparse(size(D2Om,1),size(D2Om,2)); %zeros([size(H),length(xparam1),length(xparam1)]);
D2P=sparse(size(D2Om,1),size(D2Om,2)); %zeros([size(T),length(xparam1),length(xparam1)]);
jcount=0;
end
if DynareOptions.lik_init==1,
for i=1:EstimatedParameters.nvx
k =EstimatedParameters.var_exo(i,1);
DQ(k,k,i) = 2*sqrt(Q(k,k));
dum = lyapunov_symm(T,DOm(:,:,i),DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold,[],[],DynareOptions.debug);
% kk = find(abs(dum) < 1e-12);
% dum(kk) = 0;
DP(:,:,i)=dum;
if full_Hess
for j=1:i,
jcount=jcount+1;
dum = lyapunov_symm(T,dyn_unvech(D2Om(:,jcount)),DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold,[],[],DynareOptions.debug);
% kk = (abs(dum) < 1e-12);
% dum(kk) = 0;
D2P(:,jcount)=dyn_vech(dum);
% D2P(:,:,j,i)=dum;
end
end
end
end
offset = EstimatedParameters.nvx;
for i=1:EstimatedParameters.nvn
k = EstimatedParameters.var_endo(i,1);
DH(k,k,i+offset) = 2*sqrt(H(k,k));
if full_Hess
D2H(k,k,i+offset,i+offset) = 2;
end
end
offset = offset + EstimatedParameters.nvn;
if DynareOptions.lik_init==1,
for j=1:EstimatedParameters.np
dum = lyapunov_symm(T,DT(:,:,j+offset)*Pstar*T'+T*Pstar*DT(:,:,j+offset)'+DOm(:,:,j+offset),DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold,[],[],DynareOptions.debug);
% kk = find(abs(dum) < 1e-12);
% dum(kk) = 0;
DP(:,:,j+offset)=dum;
if full_Hess
DTj = DT(:,:,j+offset);
DPj = dum;
for i=1:j+offset,
jcount=jcount+1;
DTi = DT(:,:,i);
DPi = DP(:,:,i);
D2Tij = reshape(D2T(:,jcount),size(T));
D2Omij = dyn_unvech(D2Om(:,jcount));
tmp = D2Tij*Pstar*T' + T*Pstar*D2Tij' + DTi*DPj*T' + DTj*DPi*T' + T*DPj*DTi' + T*DPi*DTj' + DTi*Pstar*DTj' + DTj*Pstar*DTi' + D2Omij;
dum = lyapunov_symm(T,tmp,DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold,[],[],DynareOptions.debug);
% dum(abs(dum)<1.e-12) = 0;
D2P(:,jcount) = dyn_vech(dum);
% D2P(:,:,j+offset,i) = dum;
end
end
end
end
if analytic_derivation==1,
analytic_deriv_info={analytic_derivation,DT,DYss,DOm,DH,DP,asy_Hess};
else
analytic_deriv_info={analytic_derivation,DT,DYss,DOm,DH,DP,D2T,D2Yss,D2Om,D2H,D2P};
clear DT DYss DOm DP D2T D2Yss D2Om D2H D2P,
end
else
analytic_deriv_info={0};
end
%------------------------------------------------------------------------------
% 4. Likelihood evaluation
%------------------------------------------------------------------------------
if ((kalman_algo==1) || (kalman_algo==3))% Multivariate Kalman Filter
if no_missing_data_flag
if DynareOptions.block
[err, LIK] = block_kalman_filter(T,R,Q,H,Pstar,Y,start,Z,kalman_tol,riccati_tol, Model.nz_state_var, Model.n_diag, Model.nobs_non_statevar);
mexErrCheck('block_kalman_filter', err);
else
[LIK,lik] = kalman_filter(Y,diffuse_periods+1,size(Y,2), ...
a,Pstar, ...
kalman_tol, riccati_tol, ...
DynareOptions.presample, ...
T,Q,R,H,Z,mm,pp,rr,Zflag,diffuse_periods, ...
analytic_deriv_info{:});
end
else
if 0 %DynareOptions.block
[err, LIK,lik] = block_kalman_filter(DatasetInfo.missing.aindex,DatasetInfo.missing.number_of_observations,DatasetInfo.missing.no_more_missing_observations,...
T,R,Q,H,Pstar,Y,start,Z,kalman_tol,riccati_tol, Model.nz_state_var, Model.n_diag, Model.nobs_non_statevar);
else
[LIK,lik] = missing_observations_kalman_filter(DatasetInfo.missing.aindex,DatasetInfo.missing.number_of_observations,DatasetInfo.missing.no_more_missing_observations,Y,diffuse_periods+1,size(Y,2), ...
a, Pstar, ...
kalman_tol, DynareOptions.riccati_tol, ...
DynareOptions.presample, ...
T,Q,R,H,Z,mm,pp,rr,Zflag,diffuse_periods);
end
end
if analytic_derivation,
LIK1=LIK;
LIK=LIK1{1};
lik1=lik;
lik=lik1{1};
end
if isinf(LIK)
if DynareOptions.use_univariate_filters_if_singularity_is_detected
if kalman_algo == 1
kalman_algo = 2;
else
kalman_algo = 4;
end
else
if isinf(LIK)
fval = Inf;
info(1) = 66;
info(2) = 0.1;
exit_flag = 0;
return
end
end
else
if DynareOptions.lik_init==3
LIK = LIK + dLIK;
if analytic_derivation==0 && nargout==2,
lik = [dlik; lik];
end
end
end
end
if (kalman_algo==2) || (kalman_algo==4)
% Univariate Kalman Filter
% resetting measurement error covariance matrix when necessary %
if ~correlated_errors_have_been_checked
if isequal(H,0)
H1 = zeros(pp,1);
mmm = mm;
if analytic_derivation,
DH = zeros(pp,length(xparam1));
end
else
if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
H1 = diag(H);
mmm = mm;
clear tmp
if analytic_derivation,
for j=1:pp,
tmp(j,:)=DH(j,j,:);
end
DH=tmp;
end
else
Z = [Z, eye(pp)];
T = blkdiag(T,zeros(pp));
Q = blkdiag(Q,H);
R = blkdiag(R,eye(pp));
Pstar = blkdiag(Pstar,H);
Pinf = blkdiag(Pinf,zeros(pp));
H1 = zeros(pp,1);
mmm = mm+pp;
end
end
if analytic_derivation,
analytic_deriv_info{5}=DH;
end
end
[LIK, lik] = univariate_kalman_filter(DatasetInfo.missing.aindex,DatasetInfo.missing.number_of_observations,DatasetInfo.missing.no_more_missing_observations,Y,diffuse_periods+1,size(Y,2), ...
a,Pstar, ...
DynareOptions.kalman_tol, ...
DynareOptions.riccati_tol, ...
DynareOptions.presample, ...
T,Q,R,H1,Z,mmm,pp,rr,Zflag,diffuse_periods,analytic_deriv_info{:});
if analytic_derivation,
LIK1=LIK;
LIK=LIK1{1};
lik1=lik;
lik=lik1{1};
end
if DynareOptions.lik_init==3
LIK = LIK+dLIK;
if analytic_derivation==0 && nargout==2,
lik = [dlik; lik];
end
end
end
if analytic_derivation
if no_DLIK==0
DLIK = LIK1{2};
% [DLIK] = score(T,R,Q,H,Pstar,Y,DT,DYss,DOm,DH,DP,start,Z,kalman_tol,riccati_tol);
end
if full_Hess ,
Hess = -LIK1{3};
% [Hess, DLL] = get_Hessian(T,R,Q,H,Pstar,Y,DT,DYss,DOm,DH,DP,D2T,D2Yss,D2Om,D2H,D2P,start,Z,kalman_tol,riccati_tol);
% Hess0 = getHessian(Y,T,DT,D2T, R*Q*transpose(R),DOm,D2Om,Z,DYss,D2Yss);
end
if asy_Hess,
% if ~((kalman_algo==2) || (kalman_algo==4)),
% [Hess] = AHessian(T,R,Q,H,Pstar,Y,DT,DYss,DOm,DH,DP,start,Z,kalman_tol,riccati_tol);
% else
Hess = LIK1{3};
% end
end
end
if isnan(LIK)
fval = Inf;
info(1) = 45;
info(2) = 0.1;
exit_flag = 0;
return
end
if imag(LIK)~=0
fval = Inf;
info(1) = 46;
info(2) = 0.1;
exit_flag = 0;
return
end
likelihood = LIK;
% ------------------------------------------------------------------------------
% 5. Adds prior if necessary
% ------------------------------------------------------------------------------
if analytic_derivation
if full_Hess,
[lnprior, dlnprior, d2lnprior] = priordens(xparam1,BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4);
Hess = Hess - d2lnprior;
else
[lnprior, dlnprior] = priordens(xparam1,BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4);
end
if no_DLIK==0
DLIK = DLIK - dlnprior';
end
if outer_product_gradient,
dlik = lik1{2};
dlik=[- dlnprior; dlik(start:end,:)];
Hess = dlik'*dlik;
end
else
lnprior = priordens(xparam1,BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4);
end
if DynareOptions.endogenous_prior==1
if DynareOptions.lik_init==2 || DynareOptions.lik_init==3
error('Endogenous prior not supported with non-stationary models')
else
[lnpriormom] = endogenous_prior(Y,Pstar,BayesInfo,H);
fval = (likelihood-lnprior-lnpriormom);
end
else
fval = (likelihood-lnprior);
end
if DynareOptions.prior_restrictions.status
tmp = feval(DynareOptions.prior_restrictions.routine, Model, DynareResults, DynareOptions, DynareDataset, DatasetInfo);
fval = fval - tmp;
end
if isnan(fval)
fval = Inf;
info(1) = 47;
info(2) = 0.1;
exit_flag = 0;
return
end
if imag(fval)~=0
fval = Inf;
info(1) = 48;
info(2) = 0.1;
exit_flag = 0;
return
end
% Update DynareOptions.kalman_algo.
DynareOptions.kalman_algo = kalman_algo;
if analytic_derivation==0 && nargout > 1
lik=lik(start:end,:);
DLIK=[-lnprior; lik(:)];
end

283
matlab/dsge_var_likelihood.m
View File

@ -1,5 +1,5 @@
function [fval,grad,hess,exit_flag,info,PHI,SIGMAu,iXX,prior] = dsge_var_likelihood(xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
% Evaluates the posterior kernel of the bvar-dsge model. Deprecated interface.
function [fval,grad,hess,exit_flag,SteadyState,trend_coeff,info,PHI,SIGMAu,iXX,prior] = dsge_var_likelihood(xparam1,DynareDataset,DynareInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults)
% Evaluates the posterior kernel of the bvar-dsge model.
%
% INPUTS
% o xparam1 [double] Vector of model's parameters.
@ -8,6 +8,10 @@ function [fval,grad,hess,exit_flag,info,PHI,SIGMAu,iXX,prior] = dsge_var_likelih
% OUTPUTS
% o fval [double] Value of the posterior kernel at xparam1.
% o cost_flag [integer] Zero if the function returns a penalty, one otherwise.
% o SteadyState [double] Steady state vector possibly recomputed
% by call to dynare_results()
% o trend_coeff [double] place holder for trend coefficients,
% currently not supported by dsge_var
% o info [integer] Vector of informations about the penalty.
% o PHI [double] Stacked BVAR-DSGE autoregressive matrices (at the mode associated to xparam1).
% o SIGMAu [double] Covariance matrix of the BVAR-DSGE (at the mode associated to xparam1).
@ -34,6 +38,275 @@ function [fval,grad,hess,exit_flag,info,PHI,SIGMAu,iXX,prior] = dsge_var_likelih
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
[fval,info,exit_flag,grad,hess,SteadyState,trend_coeff,PHI,SIGMAu,iXX,prior] = ...
dsge_var_likelihood_1(xparam1,DynareDataset,DynareInfo,DynareOptions,Model,...
EstimatedParameters,BayesInfo,BoundsInfo,DynareResults);
persistent dsge_prior_weight_idx
grad=[];
hess=[];
exit_flag = [];
info = 0;
PHI = [];
SIGMAu = [];
iXX = [];
prior = [];
SteadyState = [];
trend_coeff = [];
% Initialization of of the index for parameter dsge_prior_weight in Model.params.
if isempty(dsge_prior_weight_idx)
dsge_prior_weight_idx = strmatch('dsge_prior_weight',Model.param_names);
end
% Get the number of estimated (dsge) parameters.
nx = EstimatedParameters.nvx + EstimatedParameters.np;
% Get the number of observed variables in the VAR model.
NumberOfObservedVariables = DynareDataset.vobs;
% Get the number of observations.
NumberOfObservations = DynareDataset.nobs;
% Get the number of lags in the VAR model.
NumberOfLags = DynareOptions.dsge_varlag;
% Get the number of parameters in the VAR model.
NumberOfParameters = NumberOfObservedVariables*NumberOfLags ;
if ~DynareOptions.noconstant
NumberOfParameters = NumberOfParameters + 1;
end
% Get empirical second order moments for the observed variables.
mYY = evalin('base', 'mYY');
mYX = evalin('base', 'mYX');
mXY = evalin('base', 'mXY');
mXX = evalin('base', 'mXX');
% Initialize some of the output arguments.
fval = [];
exit_flag = 1;
% Return, with endogenous penalty, if some dsge-parameters are smaller than the lower bound of the prior domain.
if DynareOptions.mode_compute ~= 1 && any(xparam1 < BoundsInfo.lb)
fval = Inf;
exit_flag = 0;
info(1) = 41;
k = find(xparam1 < BoundsInfo.lb);
info(2) = sum((BoundsInfo.lb(k)-xparam1(k)).^2);
return;
end
% Return, with endogenous penalty, if some dsge-parameters are greater than the upper bound of the prior domain.
if DynareOptions.mode_compute ~= 1 && any(xparam1 > BoundsInfo.ub)
fval = Inf;
exit_flag = 0;
info(1) = 42;
k = find(xparam1 > BoundsInfo.ub);
info(2) = sum((xparam1(k)-BoundsInfo.ub(k)).^2);
return;
end
% Get the variance of each structural innovation.
Q = Model.Sigma_e;
for i=1:EstimatedParameters.nvx
k = EstimatedParameters.var_exo(i,1);
Q(k,k) = xparam1(i)*xparam1(i);
end
offset = EstimatedParameters.nvx;
% Update Model.params and Model.Sigma_e.
Model.params(EstimatedParameters.param_vals(:,1)) = xparam1(offset+1:end);
Model.Sigma_e = Q;
% Get the weight of the dsge prior.
dsge_prior_weight = Model.params(dsge_prior_weight_idx);
% Is the dsge prior proper?
if dsge_prior_weight<(NumberOfParameters+NumberOfObservedVariables)/ ...
NumberOfObservations;
fval = Inf;
exit_flag = 0;
info(1) = 51;
info(2) = abs(NumberOfObservations*dsge_prior_weight-(NumberOfParameters+NumberOfObservedVariables));
% info(2)=dsge_prior_weight;
% info(3)=(NumberOfParameters+NumberOfObservedVariables)/DynareDataset.nobs;
return
end
%------------------------------------------------------------------------------
% 2. call model setup & reduction program
%------------------------------------------------------------------------------
% Solve the Dsge model and get the matrices of the reduced form solution. T and R are the matrices of the
% state equation
[T,R,SteadyState,info,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults,'restrict');
% Return, with endogenous penalty when possible, if dynare_resolve issues an error code (defined in resol).
if info(1) == 1 || info(1) == 2 || info(1) == 5 || info(1) == 7 || info(1) == 8 || ...
info(1) == 22 || info(1) == 24 || info(1) == 25 || info(1) == 10
fval = Inf;
info(2) = 0.1;
exit_flag = 0;
return
elseif info(1) == 3 || info(1) == 4 || info(1) == 19 || info(1) == 20 || info(1) == 21
fval = Inf;
info(2) = 0.1;
exit_flag = 0;
return
end
% Define the mean/steady state vector.
if ~DynareOptions.noconstant
if DynareOptions.loglinear
constant = transpose(log(SteadyState(BayesInfo.mfys)));
else
constant = transpose(SteadyState(BayesInfo.mfys));
end
else
constant = zeros(1,NumberOfObservedVariables);
end
%------------------------------------------------------------------------------
% 3. theoretical moments (second order)
%------------------------------------------------------------------------------
% Compute the theoretical second order moments
tmp0 = lyapunov_symm(T,R*Q*R',DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold, [], [], DynareOptions.debug);
mf = BayesInfo.mf1;
% Get the non centered second order moments
TheoreticalAutoCovarianceOfTheObservedVariables = zeros(NumberOfObservedVariables,NumberOfObservedVariables,NumberOfLags+1);
TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1) = tmp0(mf,mf)+constant'*constant;
for lag = 1:NumberOfLags
tmp0 = T*tmp0;
TheoreticalAutoCovarianceOfTheObservedVariables(:,:,lag+1) = tmp0(mf,mf) + constant'*constant;
end
% Build the theoretical "covariance" between Y and X
GYX = zeros(NumberOfObservedVariables,NumberOfParameters);
for i=1:NumberOfLags
GYX(:,(i-1)*NumberOfObservedVariables+1:i*NumberOfObservedVariables) = TheoreticalAutoCovarianceOfTheObservedVariables(:,:,i+1);
end
if ~DynareOptions.noconstant
GYX(:,end) = constant';
end
% Build the theoretical "covariance" between X and X
GXX = kron(eye(NumberOfLags), TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1));
for i = 1:NumberOfLags-1
tmp1 = diag(ones(NumberOfLags-i,1),i);
tmp2 = diag(ones(NumberOfLags-i,1),-i);
GXX = GXX + kron(tmp1,TheoreticalAutoCovarianceOfTheObservedVariables(:,:,i+1));
GXX = GXX + kron(tmp2,TheoreticalAutoCovarianceOfTheObservedVariables(:,:,i+1)');
end
if ~DynareOptions.noconstant
% Add one row and one column to GXX
GXX = [GXX , kron(ones(NumberOfLags,1),constant') ; [ kron(ones(1,NumberOfLags),constant) , 1] ];
end
GYY = TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1);
assignin('base','GYY',GYY);
assignin('base','GXX',GXX);
assignin('base','GYX',GYX);
if ~isinf(dsge_prior_weight)% Evaluation of the likelihood of the dsge-var model when the dsge prior weight is finite.
tmp0 = dsge_prior_weight*NumberOfObservations*TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1) + mYY ;
tmp1 = dsge_prior_weight*NumberOfObservations*GYX + mYX;
tmp2 = inv(dsge_prior_weight*NumberOfObservations*GXX+mXX);
SIGMAu = tmp0 - tmp1*tmp2*tmp1'; clear('tmp0');
[SIGMAu_is_positive_definite, penalty] = ispd(SIGMAu);
if ~SIGMAu_is_positive_definite
fval = Inf;
info(1) = 52;
info(2) = penalty;
exit_flag = 0;
return;
end
SIGMAu = SIGMAu / (NumberOfObservations*(1+dsge_prior_weight));
PHI = tmp2*tmp1'; clear('tmp1');
prodlng1 = sum(gammaln(.5*((1+dsge_prior_weight)*NumberOfObservations- ...
NumberOfObservedVariables*NumberOfLags ...
+1-(1:NumberOfObservedVariables)')));
prodlng2 = sum(gammaln(.5*(dsge_prior_weight*NumberOfObservations- ...
NumberOfObservedVariables*NumberOfLags ...
+1-(1:NumberOfObservedVariables)')));
lik = .5*NumberOfObservedVariables*log(det(dsge_prior_weight*NumberOfObservations*GXX+mXX)) ...
+ .5*((dsge_prior_weight+1)*NumberOfObservations-NumberOfParameters)*log(det((dsge_prior_weight+1)*NumberOfObservations*SIGMAu)) ...
- .5*NumberOfObservedVariables*log(det(dsge_prior_weight*NumberOfObservations*GXX)) ...
- .5*(dsge_prior_weight*NumberOfObservations-NumberOfParameters)*log(det(dsge_prior_weight*NumberOfObservations*(GYY-GYX*inv(GXX)*GYX'))) ...
+ .5*NumberOfObservedVariables*NumberOfObservations*log(2*pi) ...
- .5*log(2)*NumberOfObservedVariables*((dsge_prior_weight+1)*NumberOfObservations-NumberOfParameters) ...
+ .5*log(2)*NumberOfObservedVariables*(dsge_prior_weight*NumberOfObservations-NumberOfParameters) ...
- prodlng1 + prodlng2;
else% Evaluation of the likelihood of the dsge-var model when the dsge prior weight is infinite.
iGXX = inv(GXX);
SIGMAu = GYY - GYX*iGXX*transpose(GYX);
PHI = iGXX*transpose(GYX);
lik = NumberOfObservations * ( log(det(SIGMAu)) + NumberOfObservedVariables*log(2*pi) + ...
trace(inv(SIGMAu)*(mYY - transpose(mYX*PHI) - mYX*PHI + transpose(PHI)*mXX*PHI)/NumberOfObservations));
lik = .5*lik;% Minus likelihood
end
if isnan(lik)
info(1) = 45;
info(2) = 0.1;
fval = Inf;
exit_flag = 0;
return
end
if imag(lik)~=0
info(1) = 46;
info(2) = 0.1;
fval = Inf;
exit_flag = 0;
return
end
% Add the (logged) prior density for the dsge-parameters.
lnprior = priordens(xparam1,BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4);
fval = (lik-lnprior);
if isnan(fval)
info(1) = 47;
info(2) = 0.1;
fval = Inf;
exit_flag = 0;
return
end
if imag(fval)~=0
info(1) = 48;
info(2) = 0.1;
fval = Inf;
exit_flag = 0;
return
end
if (nargout == 10)
if isinf(dsge_prior_weight)
iXX = iGXX;
else
iXX = tmp2;
end
end
if (nargout==11)
if isinf(dsge_prior_weight)
iXX = iGXX;
else
iXX = tmp2;
end
iGXX = inv(GXX);
prior.SIGMAstar = GYY - GYX*iGXX*GYX';
prior.PHIstar = iGXX*transpose(GYX);
prior.ArtificialSampleSize = fix(dsge_prior_weight*NumberOfObservations);
prior.DF = prior.ArtificialSampleSize - NumberOfParameters - NumberOfObservedVariables;
prior.iGXX = iGXX;
end
if fval == Inf
pause
end

312
matlab/dsge_var_likelihood_1.m
View File

@ -1,312 +0,0 @@
function [fval,info,exit_flag,grad,hess,SteadyState,trend_coeff,PHI,SIGMAu,iXX,prior] = dsge_var_likelihood_1(xparam1,DynareDataset,DynareInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults)
% Evaluates the posterior kernel of the bvar-dsge model.
%
% INPUTS
% o xparam1 [double] Vector of model's parameters.
% o gend [integer] Number of observations (without conditionning observations for the lags).
%
% OUTPUTS
% o fval [double] Value of the posterior kernel at xparam1.
% o cost_flag [integer] Zero if the function returns a penalty, one otherwise.
% o SteadyState [double] Steady state vector possibly recomputed
% by call to dynare_results()
% o trend_coeff [double] place holder for trend coefficients,
% currently not supported by dsge_var
% o info [integer] Vector of informations about the penalty.
% o PHI [double] Stacked BVAR-DSGE autoregressive matrices (at the mode associated to xparam1).
% o SIGMAu [double] Covariance matrix of the BVAR-DSGE (at the mode associated to xparam1).
% o iXX [double] inv(X'X).
% o prior [double] a matlab structure describing the dsge-var prior.
%
% SPECIAL REQUIREMENTS
% None.
% Copyright (C) 2006-2012 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
persistent dsge_prior_weight_idx
grad=[];
hess=[];
exit_flag = [];
info = 0;
PHI = [];
SIGMAu = [];
iXX = [];
prior = [];
SteadyState = [];
trend_coeff = [];
% Initialization of of the index for parameter dsge_prior_weight in Model.params.
if isempty(dsge_prior_weight_idx)
dsge_prior_weight_idx = strmatch('dsge_prior_weight',Model.param_names);
end
% Get the number of estimated (dsge) parameters.
nx = EstimatedParameters.nvx + EstimatedParameters.np;
% Get the number of observed variables in the VAR model.
NumberOfObservedVariables = DynareDataset.vobs;
% Get the number of observations.
NumberOfObservations = DynareDataset.nobs;
% Get the number of lags in the VAR model.
NumberOfLags = DynareOptions.dsge_varlag;
% Get the number of parameters in the VAR model.
NumberOfParameters = NumberOfObservedVariables*NumberOfLags ;
if ~DynareOptions.noconstant
NumberOfParameters = NumberOfParameters + 1;
end
% Get empirical second order moments for the observed variables.
mYY = evalin('base', 'mYY');
mYX = evalin('base', 'mYX');
mXY = evalin('base', 'mXY');
mXX = evalin('base', 'mXX');
% Initialize some of the output arguments.
fval = [];
exit_flag = 1;
% Return, with endogenous penalty, if some dsge-parameters are smaller than the lower bound of the prior domain.
if DynareOptions.mode_compute ~= 1 && any(xparam1 < BoundsInfo.lb)
fval = Inf;
exit_flag = 0;
info(1) = 41;
k = find(xparam1 < BoundsInfo.lb);
info(2) = sum((BoundsInfo.lb(k)-xparam1(k)).^2);
return;
end
% Return, with endogenous penalty, if some dsge-parameters are greater than the upper bound of the prior domain.
if DynareOptions.mode_compute ~= 1 && any(xparam1 > BoundsInfo.ub)
fval = Inf;
exit_flag = 0;
info(1) = 42;
k = find(xparam1 > BoundsInfo.ub);
info(2) = sum((xparam1(k)-BoundsInfo.ub(k)).^2);
return;
end
% Get the variance of each structural innovation.
Q = Model.Sigma_e;
for i=1:EstimatedParameters.nvx
k = EstimatedParameters.var_exo(i,1);
Q(k,k) = xparam1(i)*xparam1(i);
end
offset = EstimatedParameters.nvx;
% Update Model.params and Model.Sigma_e.
Model.params(EstimatedParameters.param_vals(:,1)) = xparam1(offset+1:end);
Model.Sigma_e = Q;
% Get the weight of the dsge prior.
dsge_prior_weight = Model.params(dsge_prior_weight_idx);
% Is the dsge prior proper?
if dsge_prior_weight<(NumberOfParameters+NumberOfObservedVariables)/ ...
NumberOfObservations;
fval = Inf;
exit_flag = 0;
info(1) = 51;
info(2) = abs(NumberOfObservations*dsge_prior_weight-(NumberOfParameters+NumberOfObservedVariables));
% info(2)=dsge_prior_weight;
% info(3)=(NumberOfParameters+NumberOfObservedVariables)/DynareDataset.nobs;
return
end
%------------------------------------------------------------------------------
% 2. call model setup & reduction program
%------------------------------------------------------------------------------
% Solve the Dsge model and get the matrices of the reduced form solution. T and R are the matrices of the
% state equation
[T,R,SteadyState,info,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults,'restrict');
% Return, with endogenous penalty when possible, if dynare_resolve issues an error code (defined in resol).
if info(1) == 1 || info(1) == 2 || info(1) == 5 || info(1) == 7 || info(1) == 8 || ...
info(1) == 22 || info(1) == 24 || info(1) == 25 || info(1) == 10
fval = Inf;
info(2) = 0.1;
exit_flag = 0;
return
elseif info(1) == 3 || info(1) == 4 || info(1) == 19 || info(1) == 20 || info(1) == 21
fval = Inf;
info(2) = 0.1;
exit_flag = 0;
return
end
% Define the mean/steady state vector.
if ~DynareOptions.noconstant
if DynareOptions.loglinear
constant = transpose(log(SteadyState(BayesInfo.mfys)));
else
constant = transpose(SteadyState(BayesInfo.mfys));
end
else
constant = zeros(1,NumberOfObservedVariables);
end
%------------------------------------------------------------------------------
% 3. theoretical moments (second order)
%------------------------------------------------------------------------------
% Compute the theoretical second order moments
tmp0 = lyapunov_symm(T,R*Q*R',DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold, [], [], DynareOptions.debug);
mf = BayesInfo.mf1;
% Get the non centered second order moments
TheoreticalAutoCovarianceOfTheObservedVariables = zeros(NumberOfObservedVariables,NumberOfObservedVariables,NumberOfLags+1);
TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1) = tmp0(mf,mf)+constant'*constant;
for lag = 1:NumberOfLags
tmp0 = T*tmp0;
TheoreticalAutoCovarianceOfTheObservedVariables(:,:,lag+1) = tmp0(mf,mf) + constant'*constant;
end
% Build the theoretical "covariance" between Y and X
GYX = zeros(NumberOfObservedVariables,NumberOfParameters);
for i=1:NumberOfLags
GYX(:,(i-1)*NumberOfObservedVariables+1:i*NumberOfObservedVariables) = TheoreticalAutoCovarianceOfTheObservedVariables(:,:,i+1);
end
if ~DynareOptions.noconstant
GYX(:,end) = constant';
end
% Build the theoretical "covariance" between X and X
GXX = kron(eye(NumberOfLags), TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1));
for i = 1:NumberOfLags-1
tmp1 = diag(ones(NumberOfLags-i,1),i);
tmp2 = diag(ones(NumberOfLags-i,1),-i);
GXX = GXX + kron(tmp1,TheoreticalAutoCovarianceOfTheObservedVariables(:,:,i+1));
GXX = GXX + kron(tmp2,TheoreticalAutoCovarianceOfTheObservedVariables(:,:,i+1)');
end
if ~DynareOptions.noconstant
% Add one row and one column to GXX
GXX = [GXX , kron(ones(NumberOfLags,1),constant') ; [ kron(ones(1,NumberOfLags),constant) , 1] ];
end
GYY = TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1);
assignin('base','GYY',GYY);
assignin('base','GXX',GXX);
assignin('base','GYX',GYX);
if ~isinf(dsge_prior_weight)% Evaluation of the likelihood of the dsge-var model when the dsge prior weight is finite.
tmp0 = dsge_prior_weight*NumberOfObservations*TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1) + mYY ;
tmp1 = dsge_prior_weight*NumberOfObservations*GYX + mYX;
tmp2 = inv(dsge_prior_weight*NumberOfObservations*GXX+mXX);
SIGMAu = tmp0 - tmp1*tmp2*tmp1'; clear('tmp0');
[SIGMAu_is_positive_definite, penalty] = ispd(SIGMAu);
if ~SIGMAu_is_positive_definite
fval = Inf;
info(1) = 52;
info(2) = penalty;
exit_flag = 0;
return;
end
SIGMAu = SIGMAu / (NumberOfObservations*(1+dsge_prior_weight));
PHI = tmp2*tmp1'; clear('tmp1');
prodlng1 = sum(gammaln(.5*((1+dsge_prior_weight)*NumberOfObservations- ...
NumberOfObservedVariables*NumberOfLags ...
+1-(1:NumberOfObservedVariables)')));
prodlng2 = sum(gammaln(.5*(dsge_prior_weight*NumberOfObservations- ...
NumberOfObservedVariables*NumberOfLags ...
+1-(1:NumberOfObservedVariables)')));
lik = .5*NumberOfObservedVariables*log(det(dsge_prior_weight*NumberOfObservations*GXX+mXX)) ...
+ .5*((dsge_prior_weight+1)*NumberOfObservations-NumberOfParameters)*log(det((dsge_prior_weight+1)*NumberOfObservations*SIGMAu)) ...
- .5*NumberOfObservedVariables*log(det(dsge_prior_weight*NumberOfObservations*GXX)) ...
- .5*(dsge_prior_weight*NumberOfObservations-NumberOfParameters)*log(det(dsge_prior_weight*NumberOfObservations*(GYY-GYX*inv(GXX)*GYX'))) ...
+ .5*NumberOfObservedVariables*NumberOfObservations*log(2*pi) ...
- .5*log(2)*NumberOfObservedVariables*((dsge_prior_weight+1)*NumberOfObservations-NumberOfParameters) ...
+ .5*log(2)*NumberOfObservedVariables*(dsge_prior_weight*NumberOfObservations-NumberOfParameters) ...
- prodlng1 + prodlng2;
else% Evaluation of the likelihood of the dsge-var model when the dsge prior weight is infinite.
iGXX = inv(GXX);
SIGMAu = GYY - GYX*iGXX*transpose(GYX);
PHI = iGXX*transpose(GYX);
lik = NumberOfObservations * ( log(det(SIGMAu)) + NumberOfObservedVariables*log(2*pi) + ...
trace(inv(SIGMAu)*(mYY - transpose(mYX*PHI) - mYX*PHI + transpose(PHI)*mXX*PHI)/NumberOfObservations));
lik = .5*lik;% Minus likelihood
end
if isnan(lik)
info(1) = 45;
info(2) = 0.1;
fval = Inf;
exit_flag = 0;
return
end
if imag(lik)~=0
info(1) = 46;
info(2) = 0.1;
fval = Inf;
exit_flag = 0;
return
end
% Add the (logged) prior density for the dsge-parameters.
lnprior = priordens(xparam1,BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4);
fval = (lik-lnprior);
if isnan(fval)
info(1) = 47;
info(2) = 0.1;
fval = Inf;
exit_flag = 0;
return
end
if imag(fval)~=0
info(1) = 48;
info(2) = 0.1;
fval = Inf;
exit_flag = 0;
return
end
if (nargout == 10)
if isinf(dsge_prior_weight)
iXX = iGXX;
else
iXX = tmp2;
end
end
if (nargout==11)
if isinf(dsge_prior_weight)
iXX = iGXX;
else
iXX = tmp2;
end
iGXX = inv(GXX);
prior.SIGMAstar = GYY - GYX*iGXX*GYX';
prior.PHIstar = iGXX*transpose(GYX);
prior.ArtificialSampleSize = fix(dsge_prior_weight*NumberOfObservations);
prior.DF = prior.ArtificialSampleSize - NumberOfParameters - NumberOfObservedVariables;
prior.iGXX = iGXX;
end
if fval == Inf
pause
end

6
matlab/dynare_estimation_1.m
View File

@ -85,10 +85,10 @@ if ~options_.dsge_var
error(['Estimation: Unknown filter ' options_.particle.filter_algorithm])
end
else
objective_function = str2func('dsge_likelihood_1');
objective_function = str2func('dsge_likelihood');
end
else
objective_function = str2func('dsge_var_likelihood_1');
objective_function = str2func('dsge_var_likelihood');
end
[dataset_, dataset_info, xparam1, hh, M_, options_, oo_, estim_params_, bayestopt_, bounds] = ...
@ -253,7 +253,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
if options_.analytic_derivation && strcmp(func2str(objective_function),'dsge_likelihood')
options = options_.analytic_derivation;
options.analytic_derivation = 2;
[junk1, junk2, junk3, junk4, hh] = feval(objective_function,xparam1, ...
[junk1, junk2, hh] = feval(objective_function,xparam1, ...
dataset_,dataset_info,options_,M_, ...
estim_params_,bayestopt_,bounds,oo_);
elseif isequal(options_.mode_compute,4) || ...

2
matlab/identification_analysis.m
View File

@ -148,7 +148,7 @@ if info(1)==0,
dataset_ = dseries(oo_.endo_simul(options_.varobs_id,100+1:end)',dates('1Q1'), options_.varobs);
derivatives_info.no_DLIK=1;
bounds = prior_bounds(bayestopt_,options_);
[fval,info,cost_flag,DLIK,AHess,ys,trend_coeff,M_,options_,bayestopt_,oo_] = dsge_likelihood(params',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_,derivatives_info);
[fval,DLIK,AHess,cost_flag,ys,trend_coeff,info,M_,options_,bayestopt_,oo_] = dsge_likelihood(params',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_,derivatives_info);
% fval = DsgeLikelihood(xparam1,data_info,options_,M_,estim_params_,bayestopt_,oo_);
options_.analytic_derivation = analytic_derivation;
AHess=-AHess;

2
matlab/mode_check.m
View File

@ -140,7 +140,7 @@ for plt = 1:nbplt,
end
for i=1:length(z)
xx(kk) = z(i);
[fval, info, exit_flag] = feval(fun,xx,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults);
[fval, junk1, junk2, exit_flag] = feval(fun,xx,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults);
if exit_flag
y(i,1) = fval;
else

12
matlab/optimization/mr_hessian.m
View File

@ -61,7 +61,7 @@ if init
return
end
[f0, exit_flag, ff0]=penalty_objective_function(x,func,penalty,varargin{:});
[f0, ff0]=penalty_objective_function(x,func,penalty,varargin{:});
h2=varargin{7}.ub-varargin{7}.lb;
hmax=varargin{7}.ub-x;
hmax=min(hmax,x-varargin{7}.lb);
@ -93,7 +93,7 @@ while i<n
hcheck=0;
xh1(i)=x(i)+h1(i);
try
[fx, exit_flag, ffx]=penalty_objective_function(xh1,func,penalty,varargin{:});
[fx, ffx]=penalty_objective_function(xh1,func,penalty,varargin{:});
catch
fx=1.e8;
end
@ -114,7 +114,7 @@ while i<n
h1(i) = max(h1(i),1.e-10);
xh1(i)=x(i)+h1(i);
try
[fx, exit_flag, ffx]=penalty_objective_function(xh1,func,penalty,varargin{:});
[fx, ffx]=penalty_objective_function(xh1,func,penalty,varargin{:});
catch
fx=1.e8;
end
@ -123,14 +123,14 @@ while i<n
h1(i)= htol/abs(dx(it))*h1(i);
xh1(i)=x(i)+h1(i);
try
[fx, exit_flag, ffx]=penalty_objective_function(xh1,func,penalty,varargin{:});
[fx, ffx]=penalty_objective_function(xh1,func,penalty,varargin{:});
catch
fx=1.e8;
end
while (fx-f0)==0
h1(i)= h1(i)*2;
xh1(i)=x(i)+h1(i);
[fx, exit_flag, ffx]=penalty_objective_function(xh1,func,penalty,varargin{:});
[fx, ffx]=penalty_objective_function(xh1,func,penalty,varargin{:});
ic=1;
end
end
@ -151,7 +151,7 @@ while i<n
end
end
xh1(i)=x(i)-h1(i);
[fx, exit_flag, ffx]=penalty_objective_function(xh1,func,penalty,varargin{:});
[fx, ffx]=penalty_objective_function(xh1,func,penalty,varargin{:});
f_1(:,i)=fx;
if outer_product_gradient,
if any(isnan(ffx)) || isempty(ffx),

6
matlab/optimization/newrat.m
View File

@ -68,7 +68,7 @@ end
% func0 = str2func([func2str(func0),'_hh']);
% func0 = func0;
[fval0,exit_flag,gg,hh]=penalty_objective_function(x,func0,penalty,varargin{:});
[fval0,gg,hh]=penalty_objective_function(x,func0,penalty,varargin{:});
fval=fval0;
% initialize mr_gstep and mr_hessian
@ -180,7 +180,7 @@ while norm(gg)>gtol && check==0 && jit<nit
disp_verbose('No further improvement is possible!',Verbose)
check=1;
if analytic_derivation,
[fvalx,exit_flag,gg,hh]=penalty_objective_function(xparam1,func0,penalty,varargin{:});
[fvalx,gg,hh]=penalty_objective_function(xparam1,func0,varargin{:});
hhg=hh;
H = inv(hh);
else
@ -256,7 +256,7 @@ while norm(gg)>gtol && check==0 && jit<nit
H = igg;
end
elseif analytic_derivation,
[fvalx,exit_flag,gg,hh]=penalty_objective_function(xparam1,func0,penalty,varargin{:});
[fvalx,gg,hh]=penalty_objective_function(xparam1,func0,varargin{:});
hhg=hh;
H = inv(hh);
end

6
matlab/optimization/penalty_objective_function.m
View File

@ -1,5 +1,7 @@
function [fval,exit_flag,arg1,arg2] = penalty_objective_function(x0,fcn,penalty,varargin)
[fval,info,exit_flag,arg1,arg2] = fcn(x0,varargin{:});
function [fval,DLIK,Hess,exit_flag] = objective_function_penalty(x0,fcn,penalty,varargin)
[fval,DLIK,Hess,exit_flag,SteadyState,trend_coeff,info] = fcn(x0,varargin{:});
if info(1) ~= 0
fval = penalty + info(2);

2
matlab/osr1.m
View File

@ -77,7 +77,7 @@ inv_order_var = oo_.dr.inv_order_var;
%extract unique entries of covariance
i_var=unique(i_var);
%% do initial checks
[loss,info,exit_flag,vx]=osr_obj(t0,i_params,inv_order_var(i_var),weights(i_var,i_var));
[loss,vx,info,exit_flag]=osr_obj(t0,i_params,inv_order_var(i_var),weights(i_var,i_var));
if info~=0
print_info(info, options_.noprint, options_);
else

60
matlab/osr_obj.m
View File

@ -1,7 +1,5 @@
function [loss,vx,info,exit_flag]=osr_obj(x,i_params,i_var,weights)
% objective function for optimal simple rules (OSR). Deprecated
% interface. New one: osr_obj_1.m
%
% objective function for optimal simple rules (OSR)
% INPUTS
% x vector values of the parameters
% over which to optimize
@ -34,4 +32,58 @@ function [loss,vx,info,exit_flag]=osr_obj(x,i_params,i_var,weights)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
[loss,info,exit_flag,vx,junk]=osr_obj_1(x,i_params,i_var,weights);
global M_ oo_ options_ optimal_Q_ it_
% global ys_ Sigma_e_ endo_nbr exo_nbr optimal_Q_ it_ ykmin_ options_
junk = [];
exit_flag = 1;
vx = [];
info=0;
loss=[];
% set parameters of the policiy rule
M_.params(i_params) = x;
% don't change below until the part where the loss function is computed
it_ = M_.maximum_lag+1;
[dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
switch info(1)
case 1
loss = 1e8;
return
case 2
loss = 1e8*min(1e3,info(2));
return
case 3
loss = 1e8*min(1e3,info(2));
return
case 4
loss = 1e8*min(1e3,info(2));
return
case 5
loss = 1e8;
return
case 6
loss = 1e8*min(1e3,info(2));
return
case 7
loss = 1e8*min(1e3);
return
case 8
loss = 1e8*min(1e3,info(2));
return
case 9
loss = 1e8*min(1e3,info(2));
return
case 20
loss = 1e8*min(1e3,info(2));
return
otherwise
if info(1)~=0
loss = 1e8;
return;
end
end
vx = get_variance_of_endogenous_variables(dr,i_var);
loss = full(weights(:)'*vx(:));

90
matlab/osr_obj_1.m
View File

@ -1,90 +0,0 @@
function [loss,info,exit_flag,vx,junk]=osr_obj_1(x,i_params,i_var,weights)
% objective function for optimal simple rules (OSR)
% INPUTS
% x vector values of the parameters
% over which to optimize
% i_params vector index of optimizing parameters in M_.params
% i_var vector variables indices
% weights vector weights in the OSRs
%
% OUTPUTS
% loss scalar loss function returned to solver
% info vector info vector returned by resol
% exit_flag scalar exit flag returned to solver
% vx vector variances of the endogenous variables
% junk empty place holder for penalty_objective_function
%
% SPECIAL REQUIREMENTS
% none
% Copyright (C) 2005-2013 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
global M_ oo_ options_ optimal_Q_ it_
% global ys_ Sigma_e_ endo_nbr exo_nbr optimal_Q_ it_ ykmin_ options_
junk = [];
exit_flag = 1;
vx = [];
info=0;
loss=[];
% set parameters of the policiy rule
M_.params(i_params) = x;
% don't change below until the part where the loss function is computed
it_ = M_.maximum_lag+1;
[dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
switch info(1)
case 1
loss = 1e8;
return
case 2
loss = 1e8*min(1e3,info(2));
return
case 3
loss = 1e8*min(1e3,info(2));
return
case 4
loss = 1e8*min(1e3,info(2));
return
case 5
loss = 1e8;
return
case 6
loss = 1e8*min(1e3,info(2));
return
case 7
loss = 1e8*min(1e3);
return
case 8
loss = 1e8*min(1e3,info(2));
return
case 9
loss = 1e8*min(1e3,info(2));
return
case 20
loss = 1e8*min(1e3,info(2));
return
otherwise
if info(1)~=0
loss = 1e8;
return;
end
end
vx = get_variance_of_endogenous_variables(dr,i_var);
loss = full(weights(:)'*vx(:));

1
tests/estimation/TaRB/fs2000_tarb.mod
View File

@ -120,4 +120,3 @@ tarb_mode_compute=4,
tarb_new_block_probability=0.3,
silent_optimizer
);