Fixed indentation.

parent 823d9474
......@@ -87,7 +87,7 @@ yhat = bsxfun(@minus,StateVectors,state_variables_steady_state);
% yhat_ = bsxfun(@minus,StateVectors_,state_variables_steady_state);
% [tmp, tmp_] = local_state_space_iteration_2(yhat,zeros(number_of_structural_innovations,number_of_particles),ghx,ghu,constant,ghxx,ghuu,ghxu,yhat_,steadystate,ThreadsOptions.local_state_space_iteration_2);
%else
tmp = local_state_space_iteration_2(yhat,zeros(number_of_structural_innovations,number_of_particles),ghx,ghu,constant,ghxx,ghuu,ghxu,ThreadsOptions.local_state_space_iteration_2);
tmp = local_state_space_iteration_2(yhat,zeros(number_of_structural_innovations,number_of_particles),ghx,ghu,constant,ghxx,ghuu,ghxu,ThreadsOptions.local_state_space_iteration_2);
%end
PredictedObservedMean = weights*(tmp(mf1,:)');
PredictionError = bsxfun(@minus,Y(:,t),tmp(mf1,:));
......
function [LIK,lik] = auxiliary_particle_filter(ReducedForm,Y,start,ParticleOptions,ThreadsOptions)
% Evaluates the likelihood of a nonlinear model with the auxiliary particle filter
% Evaluates the likelihood of a nonlinear model with the auxiliary particle filter
% allowing eventually resampling.
%
% Copyright (C) 2011-2015 Dynare Team
......@@ -91,11 +91,11 @@ end
% [nodes,nodes_weights,nodes_weights_c] = unscented_sigma_points(number_of_structural_innovations,ParticleOptions);
% else
% error('Estimation: This approximation for the proposal is not implemented or unknown!')
% end
% end
% nodes = (Q_lower_triangular_cholesky*(nodes'))' ;
nodes = zeros(1,number_of_structural_innovations) ;
nodes_weights = ones(number_of_structural_innovations,1) ;
nodes_weights = ones(number_of_structural_innovations,1) ;
for t=1:sample_size
yhat = bsxfun(@minus,StateVectors,state_variables_steady_state);
......@@ -126,7 +126,7 @@ for t=1:sample_size
yhat_ = yhat_(:,indx) ;
end
yhat = yhat(:,indx) ;
weights_stage_1 = weights(indx)./tau_tilde(indx) ;
weights_stage_1 = weights(indx)./tau_tilde(indx) ;
epsilon = Q_lower_triangular_cholesky*randn(number_of_structural_innovations,number_of_particles);
if pruning
[tmp, tmp_] = local_state_space_iteration_2(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,ghxu,yhat_,steadystate,ThreadsOptions.local_state_space_iteration_2);
......@@ -137,7 +137,7 @@ for t=1:sample_size
StateVectors = tmp(mf0,:);
PredictionError = bsxfun(@minus,Y(:,t),tmp(mf1,:));
weights_stage_2 = weights_stage_1.*(exp(-.5*(const_lik+sum(PredictionError.*(H\PredictionError),1))) + 1e-99) ;
lik(t) = log(mean(weights_stage_2)) ;
lik(t) = log(mean(weights_stage_2)) ;
weights = weights_stage_2/sum(weights_stage_2);
if (ParticleOptions.resampling.status.generic && neff(weights)<ParticleOptions.resampling.threshold*sample_size) || ParticleOptions.resampling.status.systematic
if pruning
......
......@@ -51,8 +51,8 @@ ghuu = ReducedForm.ghuu;
ghxu = ReducedForm.ghxu;
if any(any(isnan(ghx))) || any(any(isnan(ghu))) || any(any(isnan(ghxx))) || any(any(isnan(ghuu))) || any(any(isnan(ghxu))) || ...
any(any(isinf(ghx))) || any(any(isinf(ghu))) || any(any(isinf(ghxx))) || any(any(isinf(ghuu))) || any(any(isinf(ghxu))) ...
any(any(abs(ghx)>1e4)) || any(any(abs(ghu)>1e4)) || any(any(abs(ghxx)>1e4)) || any(any(abs(ghuu)>1e4)) || any(any(abs(ghxu)>1e4))
any(any(isinf(ghx))) || any(any(isinf(ghu))) || any(any(isinf(ghxx))) || any(any(isinf(ghuu))) || any(any(isinf(ghxu))) ...
any(any(abs(ghx)>1e4)) || any(any(abs(ghu)>1e4)) || any(any(abs(ghxx)>1e4)) || any(any(abs(ghuu)>1e4)) || any(any(abs(ghxu)>1e4))
ghx
ghu
ghxx
......@@ -106,7 +106,7 @@ if ParticleOptions.proposal_approximation.cubature || ParticleOptions.proposal_a
Error = obs - PredictedObservedMean ;
StateVectorMean = PredictedStateMean + (CovarianceObservedStateSquareRoot/PredictedObservedVarianceSquareRoot)*Error ;
if ParticleOptions.cpf_weights_method.amisanotristani
Weights = SampleWeights.*probability2(zeros(number_of_observed_variables,1),PredictedObservedVarianceSquareRoot,Error) ;
Weights = SampleWeights.*probability2(zeros(number_of_observed_variables,1),PredictedObservedVarianceSquareRoot,Error) ;
end
else
dState = bsxfun(@minus,tmp(mf0,:),PredictedStateMean);
......@@ -120,15 +120,15 @@ else
StateVectorVariance = PredictedStateVariance - KalmanFilterGain*PredictedObservedVariance*KalmanFilterGain';
StateVectorVarianceSquareRoot = chol(StateVectorVariance + eye(number_of_state_variables)*1e-6)' ;
if ParticleOptions.cpf_weights_method.amisanotristani
Weights = SampleWeights.*probability2(zeros(number_of_observed_variables,1),chol(PredictedObservedVariance)',Error) ;
Weights = SampleWeights.*probability2(zeros(number_of_observed_variables,1),chol(PredictedObservedVariance)',Error) ;
end
end
PredictedStateVarianceSquareRoot = chol(PredictedStateVariance + eye(number_of_state_variables)*1e-6)' ;
ProposalStateVector = StateVectorVarianceSquareRoot*randn(size(StateVectorVarianceSquareRoot,2),1)+StateVectorMean ;
if ParticleOptions.cpf_weights_method.murrayjonesparslow
Prior = probability2(PredictedStateMean,PredictedStateVarianceSquareRoot,ProposalStateVector) ;
Posterior = probability2(StateVectorMean,StateVectorVarianceSquareRoot,ProposalStateVector) ;
Likelihood = probability2(obs,H_lower_triangular_cholesky,measurement_equations(ProposalStateVector,ReducedForm,ThreadsOptions)) ;
Prior = probability2(PredictedStateMean,PredictedStateVarianceSquareRoot,ProposalStateVector) ;
Posterior = probability2(StateVectorMean,StateVectorVarianceSquareRoot,ProposalStateVector) ;
Likelihood = probability2(obs,H_lower_triangular_cholesky,measurement_equations(ProposalStateVector,ReducedForm,ThreadsOptions)) ;
Weights = SampleWeights.*Likelihood.*(Prior./Posterior) ;
end
function [LIK,lik] = conditional_particle_filter(ReducedForm,Y,start,ParticleOptions,ThreadsOptions)
%
%
% Evaluates the likelihood of a non-linear model with a particle filter
% - the proposal is built using the Kalman updating step for each particle.
% - we need draws in the errors distributions
% Whether we use Monte-Carlo draws from a multivariate gaussian distribution
% as in Amisano & Tristani (JEDC 2010).
% Whether we use multidimensional Gaussian sparse grids approximations:
% - a univariate Kronrod-Paterson Gaussian quadrature combined by the Smolyak
% operator (ref: Winschel & Kratzig, 2010).
% - the proposal is built using the Kalman updating step for each particle.
% - we need draws in the errors distributions
% Whether we use Monte-Carlo draws from a multivariate gaussian distribution
% as in Amisano & Tristani (JEDC 2010).
% Whether we use multidimensional Gaussian sparse grids approximations:
% - a univariate Kronrod-Paterson Gaussian quadrature combined by the Smolyak
% operator (ref: Winschel & Kratzig, 2010).
% - a spherical-radial cubature (ref: Arasaratnam & Haykin, 2009a,2009b).
% - a scaled unscented transform cubature (ref: Julier & Uhlmann 1997, van der
% - a scaled unscented transform cubature (ref: Julier & Uhlmann 1997, van der
% Merwe & Wan 2003).
%
% Pros:
% - Allows using current observable information in the proposal
% - The use of sparse grids Gaussian approximation is much faster than the Monte-Carlo approach
% Cons:
% - The use of the Kalman updating step may biais the proposal distribution since
%
% Pros:
% - Allows using current observable information in the proposal
% - The use of sparse grids Gaussian approximation is much faster than the Monte-Carlo approach
% Cons:
% - The use of the Kalman updating step may biais the proposal distribution since
% it has been derived in a linear context and is implemented in a nonlinear
% context. That is why particle resampling is performed.
% context. That is why particle resampling is performed.
%
% INPUTS
% reduced_form_model [structure] Matlab's structure describing the reduced form model.
......@@ -58,8 +58,8 @@ function [LIK,lik] = conditional_particle_filter(ReducedForm,Y,start,ParticleOpt
% stephane DOT adjemian AT univ DASH lemans DOT fr
persistent init_flag mf1
persistent number_of_particles
persistent sample_size number_of_observed_variables
persistent number_of_particles
persistent sample_size number_of_observed_variables
% Set default
if isempty(start)
......@@ -82,14 +82,14 @@ if isempty(H)
H = 0;
H_lower_triangular_cholesky = 0;
else
H_lower_triangular_cholesky = chol(H)';
H_lower_triangular_cholesky = chol(H)';
end
% Get initial condition for the state vector.
StateVectorMean = ReducedForm.StateVectorMean;
StateVectorVarianceSquareRoot = chol(ReducedForm.StateVectorVariance)';
state_variance_rank = size(StateVectorVarianceSquareRoot,2);
Q_lower_triangular_cholesky = chol(Q)';
Q_lower_triangular_cholesky = chol(Q)';
% Set seed for randn().
set_dynare_seed('default');
......@@ -102,13 +102,13 @@ ks = 0 ;
StateParticles = bsxfun(@plus,StateVectorVarianceSquareRoot*randn(state_variance_rank,number_of_particles),StateVectorMean);
SampleWeights = ones(1,number_of_particles)/number_of_particles ;
for t=1:sample_size
for i=1:number_of_particles
[StateParticles(:,i),SampleWeights(i)] = ...
conditional_filter_proposal(ReducedForm,Y(:,t),StateParticles(:,i),SampleWeights(i),Q_lower_triangular_cholesky,H_lower_triangular_cholesky,H,ParticleOptions,ThreadsOptions,normconst2) ;
for i=1:number_of_particles
[StateParticles(:,i),SampleWeights(i)] = ...
conditional_filter_proposal(ReducedForm,Y(:,t),StateParticles(:,i),SampleWeights(i),Q_lower_triangular_cholesky,H_lower_triangular_cholesky,H,ParticleOptions,ThreadsOptions,normconst2) ;
end
SumSampleWeights = sum(SampleWeights) ;
lik(t) = log(SumSampleWeights) ;
SampleWeights = SampleWeights./SumSampleWeights ;
lik(t) = log(SumSampleWeights) ;
SampleWeights = SampleWeights./SumSampleWeights ;
if (ParticleOptions.resampling.status.generic && neff(SampleWeights)<ParticleOptions.resampling.threshold*sample_size) || ParticleOptions.resampling.status.systematic
ks = ks + 1 ;
StateParticles = resample(StateParticles',SampleWeights',ParticleOptions)';
......
function [StateMu,StateSqrtP,StateWeights] = fit_gaussian_mixture(X,X_weights,StateMu,StateSqrtP,StateWeights,crit,niters,check)
function [StateMu,StateSqrtP,StateWeights] = fit_gaussian_mixture(X,X_weights,StateMu,StateSqrtP,StateWeights,crit,niters,check)
% Copyright (C) 2013 Dynare Team
%
......@@ -17,36 +17,35 @@ function [StateMu,StateSqrtP,StateWeights] = fit_gaussian_mixture(X,X_weights,St
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
[dim,Ndata] = size(X);
[dim,Ndata] = size(X);
M = size(StateMu,2) ;
if check % Ensure that covariances don't collapse
MIN_COVAR_SQRT = sqrt(eps);
init_covars = StateSqrtP;
MIN_COVAR_SQRT = sqrt(eps);
init_covars = StateSqrtP;
end
eold = -Inf;
for n=1:niters
% Calculate posteriors based on old parameters
[prior,likelihood,marginal,posterior] = probability3(StateMu,StateSqrtP,StateWeights,X,X_weights);
e = sum(log(marginal));
if (n > 1 && abs((e - eold)/eold) < crit)
return;
else
eold = e;
end
new_pr = (sum(posterior,2))';
StateWeights = new_pr/Ndata;
StateMu = bsxfun(@rdivide,(posterior*X')',new_pr);
for j=1:M
diffs = bsxfun(@minus,X,StateMu(:,j));
tpost = (1/sqrt(new_pr(j)))*sqrt(posterior(j,:));
diffs = bsxfun(@times,diffs,tpost);
[foo,tcov] = qr2(diffs',0);
StateSqrtP(:,:,j) = tcov';
if check
if min(abs(diag(StateSqrtP(:,:,j)))) < MIN_COVAR_SQRT
StateSqrtP(:,:,j) = init_covars(:,:,j);
end
% Calculate posteriors based on old parameters
[prior,likelihood,marginal,posterior] = probability3(StateMu,StateSqrtP,StateWeights,X,X_weights);
e = sum(log(marginal));
if (n > 1 && abs((e - eold)/eold) < crit)
return;
else
eold = e;
end
end
end
new_pr = (sum(posterior,2))';
StateWeights = new_pr/Ndata;
StateMu = bsxfun(@rdivide,(posterior*X')',new_pr);
for j=1:M
diffs = bsxfun(@minus,X,StateMu(:,j));
tpost = (1/sqrt(new_pr(j)))*sqrt(posterior(j,:));
diffs = bsxfun(@times,diffs,tpost);
[foo,tcov] = qr2(diffs',0);
StateSqrtP(:,:,j) = tcov';
if check
if min(abs(diag(StateSqrtP(:,:,j)))) < MIN_COVAR_SQRT
StateSqrtP(:,:,j) = init_covars(:,:,j);
end
end
end
end
function IncrementalWeights = gaussian_densities(obs,mut_t,sqr_Pss_t_t,st_t_1,sqr_Pss_t_t_1,particles,H,normconst,weigths1,weigths2,ReducedForm,ThreadsOptions)
%
% Elements to calculate the importance sampling ratio
% Elements to calculate the importance sampling ratio
%
% INPUTS
% reduced_form_model [structure] Matlab's structure describing the reduced form model.
......@@ -36,11 +36,11 @@ function IncrementalWeights = gaussian_densities(obs,mut_t,sqr_Pss_t_t,st_t_1,sq
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
% proposal density
proposal = probability2(mut_t,sqr_Pss_t_t,particles) ;
% prior density
prior = probability2(st_t_1,sqr_Pss_t_t_1,particles) ;
% likelihood
% proposal density
proposal = probability2(mut_t,sqr_Pss_t_t,particles) ;
% prior density
prior = probability2(st_t_1,sqr_Pss_t_t_1,particles) ;
% likelihood
yt_t_1_i = measurement_equations(particles,ReducedForm,ThreadsOptions) ;
eta_t_i = bsxfun(@minus,obs,yt_t_1_i)' ;
yt_t_1 = sum(yt_t_1_i*weigths1,2) ;
......@@ -48,5 +48,5 @@ tmp = bsxfun(@minus,yt_t_1_i,yt_t_1) ;
Pyy = bsxfun(@times,weigths2',tmp)*tmp' + H ;
sqr_det = sqrt(det(Pyy)) ;
foo = (eta_t_i/Pyy).*eta_t_i ;
likelihood = exp(-0.5*sum(foo,2))/(normconst*sqr_det) + 1e-99 ;
likelihood = exp(-0.5*sum(foo,2))/(normconst*sqr_det) + 1e-99 ;
IncrementalWeights = likelihood.*prior./proposal ;
......@@ -112,18 +112,18 @@ for t=1:sample_size
if ParticleOptions.distribution_approximation.cubature || ParticleOptions.distribution_approximation.unscented
StateParticles = bsxfun(@plus,StateVectorMean,StateVectorVarianceSquareRoot*nodes2') ;
IncrementalWeights = ...
gaussian_densities(Y(:,t),StateVectorMean,...
StateVectorVarianceSquareRoot,PredictedStateMean,...
PredictedStateVarianceSquareRoot,StateParticles,H,const_lik,...
weights2,weights_c2,ReducedForm,ThreadsOptions) ;
gaussian_densities(Y(:,t),StateVectorMean,...
StateVectorVarianceSquareRoot,PredictedStateMean,...
PredictedStateVarianceSquareRoot,StateParticles,H,const_lik,...
weights2,weights_c2,ReducedForm,ThreadsOptions) ;
SampleWeights = weights2.*IncrementalWeights ;
else
else
StateParticles = bsxfun(@plus,StateVectorVarianceSquareRoot*randn(state_variance_rank,number_of_particles),StateVectorMean) ;
IncrementalWeights = ...
gaussian_densities(Y(:,t),StateVectorMean,...
StateVectorVarianceSquareRoot,PredictedStateMean,...
PredictedStateVarianceSquareRoot,StateParticles,H,const_lik,...
1/number_of_particles,1/number_of_particles,ReducedForm,ThreadsOptions) ;
gaussian_densities(Y(:,t),StateVectorMean,...
StateVectorVarianceSquareRoot,PredictedStateMean,...
PredictedStateVarianceSquareRoot,StateParticles,H,const_lik,...
1/number_of_particles,1/number_of_particles,ReducedForm,ThreadsOptions) ;
SampleWeights = IncrementalWeights/number_of_particles ;
end
SampleWeights = SampleWeights + 1e-6*ones(size(SampleWeights,1),1) ;
......@@ -132,9 +132,9 @@ for t=1:sample_size
SampleWeights = SampleWeights./SumSampleWeights ;
if not(ParticleOptions.distribution_approximation.cubature || ParticleOptions.distribution_approximation.unscented)
if (ParticleOptions.resampling.status.generic && neff(SampleWeights)<ParticleOptions.resampling.threshold*sample_size) || ParticleOptions.resampling.status.systematic
StateParticles = resample(StateParticles',SampleWeights,ParticleOptions)' ;
StateParticles = resample(StateParticles',SampleWeights,ParticleOptions)' ;
SampleWeights = ones(number_of_particles,1)/number_of_particles;
end
end
end
StateVectorMean = StateParticles*SampleWeights ;
temp = bsxfun(@minus,StateParticles,StateVectorMean) ;
......
......@@ -49,8 +49,8 @@ ghuu = ReducedForm.ghuu;
ghxu = ReducedForm.ghxu;
if any(any(isnan(ghx))) || any(any(isnan(ghu))) || any(any(isnan(ghxx))) || any(any(isnan(ghuu))) || any(any(isnan(ghxu))) || ...
any(any(isinf(ghx))) || any(any(isinf(ghu))) || any(any(isinf(ghxx))) || any(any(isinf(ghuu))) || any(any(isinf(ghxu))) ...
any(any(abs(ghx)>1e4)) || any(any(abs(ghu)>1e4)) || any(any(abs(ghxx)>1e4)) || any(any(abs(ghuu)>1e4)) || any(any(abs(ghxu)>1e4))
any(any(isinf(ghx))) || any(any(isinf(ghu))) || any(any(isinf(ghxx))) || any(any(isinf(ghuu))) || any(any(isinf(ghxu))) ...
any(any(abs(ghx)>1e4)) || any(any(abs(ghu)>1e4)) || any(any(abs(ghxx)>1e4)) || any(any(abs(ghuu)>1e4)) || any(any(abs(ghxu)>1e4))
ghx
ghu
ghxx
......
function IncrementalWeights = gaussian_mixture_densities(obs,StateMuPrior,StateSqrtPPrior,StateWeightsPrior,...
StateMuPost,StateSqrtPPost,StateWeightsPost,...
StateParticles,H,normconst,weigths1,weigths2,ReducedForm,ThreadsOptions)
StateMuPost,StateSqrtPPost,StateWeightsPost,...
StateParticles,H,normconst,weigths1,weigths2,ReducedForm,ThreadsOptions)
%
% Elements to calculate the importance sampling ratio
% Elements to calculate the importance sampling ratio
%
% INPUTS
% reduced_form_model [structure] Matlab's structure describing the reduced form model.
......@@ -38,11 +38,11 @@ function IncrementalWeights = gaussian_mixture_densities(obs,StateMuPrior,State
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
% Compute the density of particles under the prior distribution
[ras,ras,prior] = probability(StateMuPrior,StateSqrtPPrior,StateWeightsPrior,StateParticles) ;
% Compute the density of particles under the prior distribution
[ras,ras,prior] = probability(StateMuPrior,StateSqrtPPrior,StateWeightsPrior,StateParticles) ;
prior = prior' ;
% Compute the density of particles under the proposal distribution
[ras,ras,proposal] = probability(StateMuPost,StateSqrtPPost,StateWeightsPost,StateParticles) ;
% Compute the density of particles under the proposal distribution
[ras,ras,proposal] = probability(StateMuPost,StateSqrtPPost,StateWeightsPost,StateParticles) ;
proposal = proposal' ;
% Compute the density of the current observation conditionally to each particle
yt_t_1_i = measurement_equations(StateParticles,ReducedForm,ThreadsOptions) ;
......@@ -52,6 +52,5 @@ tmp = bsxfun(@minus,yt_t_1_i,yt_t_1) ;
Pyy = bsxfun(@times,weigths2',tmp)*tmp' + H ;
sqr_det = sqrt(det(Pyy)) ;
foo = (eta_t_i/Pyy).*eta_t_i ;
likelihood = exp(-0.5*sum(foo,2))/(normconst*sqr_det) + 1e-99 ;
likelihood = exp(-0.5*sum(foo,2))/(normconst*sqr_det) + 1e-99 ;
IncrementalWeights = likelihood.*prior./proposal ;
......@@ -63,15 +63,15 @@ end
% Set persistent variables.
if isempty(init_flag)
mf0 = ReducedForm.mf0;
mf1 = ReducedForm.mf1;
sample_size = size(Y,2);
number_of_state_variables = length(mf0);
number_of_observed_variables = length(mf1);
number_of_structural_innovations = length(ReducedForm.Q);
G = ParticleOptions.mixture_state_variables; % number of GM components in state
number_of_particles = ParticleOptions.number_of_particles;
init_flag = 1;
mf0 = ReducedForm.mf0;
mf1 = ReducedForm.mf1;
sample_size = size(Y,2);
number_of_state_variables = length(mf0);
number_of_observed_variables = length(mf1);
number_of_structural_innovations = length(ReducedForm.Q);
G = ParticleOptions.mixture_state_variables; % number of GM components in state
number_of_particles = ParticleOptions.number_of_particles;
init_flag = 1;
end
% compute gaussian quadrature nodes and weights on states and shocks
......@@ -104,14 +104,14 @@ else
end
Q_lower_triangular_cholesky = reduced_rank_cholesky(Q)';
% Initialize mixtures
% Initialize mixtures
StateWeights = ones(1,G)/G ;
StateMu = ReducedForm.StateVectorMean ;
StateSqrtP = zeros(number_of_state_variables,number_of_state_variables,G) ;
temp = reduced_rank_cholesky(ReducedForm.StateVectorVariance)' ;
StateMu = bsxfun(@plus,StateMu,bsxfun(@times,diag(temp),(-(G-1)/2:1:(G-1)/2))/10) ;
for g=1:G
StateSqrtP(:,:,g) = temp/sqrt(G) ;
StateSqrtP(:,:,g) = temp/sqrt(G) ;
end
% if ParticleOptions.mixture_structural_shocks==1
......@@ -135,11 +135,11 @@ end
% for i=1:I
% StructuralShocksSqrtP(:,:,i) = Q_lower_triangular_cholesky/sqrt(StructuralShocksWeights(i)) ;
% end
%
% if ParticleOptions.mixture_measurement_shocks==1
%
% if ParticleOptions.mixture_measurement_shocks==1
% ObservationShocksMu = zeros(1,number_of_observed_variables) ;
% ObservationShocksWeights = 1 ;
% else
% else
% if ParticleOptions.proposal_approximation.cubature
% [ObservationShocksMu,ObservationShocksWeights] = spherical_radial_sigma_points(number_of_observed_variables);
% ObservationShocksWeights = ones(size(ObservationShocksMu,1),1)*ObservationShocksWeights;
......@@ -150,7 +150,7 @@ end
% error('Estimation: This approximation for the proposal is not implemented or unknown!')
% end
% end
% end
% end
% J = size(ObservationShocksWeights,1) ;
% ObservationShocksMu = H_lower_triangular_cholesky*(ObservationShocksMu') ;
% ObservationShocksSqrtP = zeros(number_of_observed_variables,number_of_observed_variables,J) ;
......@@ -180,7 +180,7 @@ elseif ParticleOptions.mixture_structural_shocks==1
StructuralShocksMu = Q_lower_triangular_cholesky*(StructuralShocksMu') ;
StructuralShocksSqrtP = zeros(number_of_structural_innovations,number_of_structural_innovations,I) ;
for i=1:I
StructuralShocksSqrtP(:,:,i) = Q_lower_triangular_cholesky ;
StructuralShocksSqrtP(:,:,i) = Q_lower_triangular_cholesky ;
end
else
if ParticleOptions.proposal_approximation.cubature
......@@ -197,7 +197,7 @@ else
StructuralShocksMu = Q_lower_triangular_cholesky*(StructuralShocksMu') ;
StructuralShocksSqrtP = zeros(number_of_structural_innovations,number_of_structural_innovations,I) ;
for i=1:I
StructuralShocksSqrtP(:,:,i) = Q_lower_triangular_cholesky/sqrt(StructuralShocksWeights(i)) ;
StructuralShocksSqrtP(:,:,i) = Q_lower_triangular_cholesky/sqrt(StructuralShocksWeights(i)) ;
end
end
......@@ -208,14 +208,14 @@ ObservationShocksMu = H_lower_triangular_cholesky*(ObservationShocksMu') ;
ObservationShocksSqrtP = zeros(number_of_observed_variables,number_of_observed_variables,J) ;
ObservationShocksSqrtP(:,:,1) = H_lower_triangular_cholesky ;
% if ParticleOptions.mixture_measurement_shocks==0
% if ParticleOptions.mixture_measurement_shocks==0
% ObservationShocksMu = zeros(1,number_of_observed_variables) ;
% ObservationShocksWeights = 1 ;
% J = 1 ;
% ObservationShocksMu = H_lower_triangular_cholesky*(ObservationShocksMu') ;
% ObservationShocksSqrtP = zeros(number_of_observed_variables,number_of_observed_variables,J) ;
% ObservationShocksSqrtP(:,:,1) = H_lower_triangular_cholesky ;
% elseif ParticleOptions.mixture_measurement_shocks==1
% elseif ParticleOptions.mixture_measurement_shocks==1
% if ParticleOptions.proposal_approximation.cubature
% [ObservationShocksMu,ObservationShocksWeights] = spherical_radial_sigma_points(number_of_observed_variables);
% ObservationShocksWeights = ones(size(ObservationShocksMu,1),1)*ObservationShocksWeights;
......@@ -232,7 +232,7 @@ ObservationShocksSqrtP(:,:,1) = H_lower_triangular_cholesky ;
% for j=1:J
% ObservationShocksSqrtP(:,:,j) = H_lower_triangular_cholesky ;
% end
% else
% else
% if ParticleOptions.proposal_approximation.cubature
% [ObservationShocksMu,ObservationShocksWeights] = spherical_radial_sigma_points(number_of_observed_variables);
% ObservationShocksWeights = ones(size(ObservationShocksMu,1),1)*ObservationShocksWeights;
......@@ -277,10 +277,10 @@ for t=1:sample_size
gsecond = gprime + (j-1)*Gprime ;
[StateMuPrior(:,gprime),StateSqrtPPrior(:,:,gprime),StateWeightsPrior(1,gprime),...
StateMuPost(:,gsecond),StateSqrtPPost(:,:,gsecond),StateWeightsPost(1,gsecond)] =...
gaussian_mixture_filter_bank(ReducedForm,Y(:,t),StateMu(:,g),StateSqrtP(:,:,g),StateWeights(g),...
StructuralShocksMu(:,i),StructuralShocksSqrtP(:,:,i),StructuralShocksWeights(i),...
ObservationShocksMu(:,j),ObservationShocksSqrtP(:,:,j),ObservationShocksWeights(j),...
H,H_lower_triangular_cholesky,const_lik,ParticleOptions,ThreadsOptions) ;
gaussian_mixture_filter_bank(ReducedForm,Y(:,t),StateMu(:,g),StateSqrtP(:,:,g),StateWeights(g),...
StructuralShocksMu(:,i),StructuralShocksSqrtP(:,:,i),StructuralShocksWeights(i),...
ObservationShocksMu(:,j),ObservationShocksSqrtP(:,:,j),ObservationShocksWeights(j),...
H,H_lower_triangular_cholesky,const_lik,ParticleOptions,ThreadsOptions) ;
end
end
end
......@@ -293,8 +293,8 @@ for t=1:sample_size
for i=1:Gsecond
StateParticles = bsxfun(@plus,StateMuPost(:,i),StateSqrtPPost(:,:,i)*nodes') ;
IncrementalWeights = gaussian_mixture_densities(Y(:,t),StateMuPrior,StateSqrtPPrior,StateWeightsPrior,...
StateMuPost,StateSqrtPPost,StateWeightsPost,...
StateParticles,H,const_lik,weights,weights_c,ReducedForm,ThreadsOptions) ;
StateMuPost,StateSqrtPPost,StateWeightsPost,...
StateParticles,H,const_lik,weights,weights_c,ReducedForm,ThreadsOptions) ;
SampleWeights(i) = sum(StateWeightsPost(i)*weights.*IncrementalWeights) ;
end
SumSampleWeights = sum(SampleWeights) ;
......@@ -311,9 +311,9 @@ for t=1:sample_size
% Sample particle in the proposal distribution, ie the posterior state GM
StateParticles = importance_sampling(StateMuPost,StateSqrtPPost,StateWeightsPost',number_of_particles) ;
IncrementalWeights = gaussian_mixture_densities(Y(:,t),StateMuPrior,StateSqrtPPrior,StateWeightsPrior,...
StateMuPost,StateSqrtPPost,StateWeightsPost,...
StateParticles,H,const_lik,1/number_of_particles,...
1/number_of_particles,ReducedForm,ThreadsOptions) ;
StateMuPost,StateSqrtPPost,StateWeightsPost,...
StateParticles,H,const_lik,1/number_of_particles,...
1/number_of_particles,ReducedForm,ThreadsOptions) ;
SampleWeights = IncrementalWeights/number_of_particles ;
SumSampleWeights = sum(SampleWeights,1) ;
SampleWeights = SampleWeights./SumSampleWeights ;
......
function [StateMuPrior,StateSqrtPPrior,StateWeightsPrior,StateMuPost,StateSqrtPPost,StateWeightsPost] =...
gaussian_mixture_filter_bank(ReducedForm,obs,StateMu,StateSqrtP,StateWeights,...
StructuralShocksMu,StructuralShocksSqrtP,StructuralShocksWeights,...
ObservationShocksMu,ObservationShocksSqrtP,ObservationShocksWeights,...
H,H_lower_triangular_cholesky,normfactO,ParticleOptions,ThreadsOptions)
gaussian_mixture_filter_bank(ReducedForm,obs,StateMu,StateSqrtP,StateWeights,...
StructuralShocksMu,StructuralShocksSqrtP,StructuralShocksWeights,...
ObservationShocksMu,ObservationShocksSqrtP,ObservationShocksWeights,...
H,H_lower_triangular_cholesky,normfactO,ParticleOptions,ThreadsOptions)
%
% Computes the proposal with a gaussian approximation for importance
% sampling
% This proposal is a gaussian distribution calculated à la Kalman
% sampling
% This proposal is a gaussian distribution calculated à la Kalman
%
% INPUTS
% reduced_form_model [structure] Matlab's structure describing the reduced form model.
......@@ -43,8 +43,8 @@ function [StateMuPrior,StateSqrtPPrior,StateWeightsPrior,StateMuPost,StateSqrtPP
persistent init_flag2 mf0 mf1 %nodes3 weights3 weights_c3
persistent number_of_state_variables number_of_observed_variables
persistent number_of_structural_innovations
persistent number_of_state_variables number_of_observed_variables
persistent number_of_structural_innovations
% Set local state space model (first-order approximation).
ghx = ReducedForm.ghx;
......@@ -55,8 +55,8 @@ ghuu = ReducedForm.ghuu;
ghxu = ReducedForm.ghxu;
if any(any(isnan(ghx))) || any(any(isnan(ghu))) || any(any(isnan(ghxx))) || any(any(isnan(ghuu))) || any(any(isnan(ghxu))) || ...
any(any(isinf(ghx))) || any(any(isinf(ghu))) || any(any(isinf(ghxx))) || any(any(isinf(ghuu))) || any(any(isinf(ghxu))) ...
any(any(abs(ghx)>1e4)) || any(any(abs(ghu)>1e4)) || any(any(abs(ghxx)>1e4)) || any(any(abs(ghuu)>1e4)) || any(any(abs(ghxu)>1e4))
any(any(isinf(ghx))) || any(any(isinf(ghu))) || any(any(isinf(ghxx))) || any(any(isinf(ghuu))) || any(any(isinf(ghxu))) ...
any(any(abs(ghx)>1e4)) || any(any(abs(ghu)>1e4)) || any(any(abs(ghxx)>1e4)) || any(any(abs(ghuu)>1e4)) || any(any(abs(ghxu)>1e4))
ghx
ghu
ghxx
......
......@@ -17,13 +17,12 @@ function State_Particles = importance_sampling(StateMuPost,StateSqrtPPost,StateW
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
[Xdim,Gsecond] = size(StateMuPost) ;
[Xdim,Gsecond] = size(StateMuPost) ;
u = rand(numP,1);
[Nc,comp] = histc(u, cumsum([0; StateWeightsPost]));
[Nc,comp] = histc(u, cumsum([0; StateWeightsPost]));
State_Particles = zeros(Xdim,numP);
for k=1:Gsecond
idx = bsxfun(@eq,comp,k*ones(size(comp))) ;
State_Particles(:,idx) = StateSqrtPPost(:,:,k)*randn(Xdim,Nc(k));
idx = bsxfun(@eq,comp,k*ones(size(comp))) ;
State_Particles(:,idx) = StateSqrtPPost(:,:,k)*randn(Xdim,Nc(k));
end
State_Particles= State_Particles + StateMuPost(:,comp);
State_Particles= State_Particles + StateMuPost(:,comp);
function measure = measurement_equations(StateVectors,ReducedForm,ThreadsOptions)
function measure = measurement_equations(StateVectors,ReducedForm,ThreadsOptions)
% Copyright (C) 2013 Dynare Team
%
......@@ -28,6 +28,3 @@ state_variables_steady_state = ReducedForm.state_variables_steady_state;
number_of_structural_innovations = length(ReducedForm.Q);
yhat = bsxfun(@minus,StateVectors,state_variables_steady_state) ;
measure = local_state_space_iteration_2(yhat,zeros(number_of_structural_innovations,size(yhat,2)),ghx,ghu,constant,ghxx,ghuu,ghxu,ThreadsOptions.local_state_space_iteration_2);
......@@ -63,10 +63,10 @@ number_of_states = size(particles,2);
[P,D] = eig(particles'*(bsxfun(@times,1/number_of_particles,particles))) ;
D = diag(D) ;
vectors = bsxfun(@times,P,sqrt(D)') ;
orthogonalized_particles = bsxfun(@rdivide,particles*vectors,D') ;
orthogonalized_particles = bsxfun(@rdivide,particles*vectors,D') ;
new_particles = zeros(number_of_particles,number_of_states) ;
for j=1:number_of_states
tout = sortrows( [orthogonalized_particles(:,j) weights],1) ;
new_particles(:,j) = univariate_smooth_resampling(tout(:,2),tout(:,1),number_of_particles) ;
tout = sortrows( [orthogonalized_particles(:,j) weights],1) ;
new_particles(:,j) = univariate_smooth_resampling(tout(:,2),tout(:,1),number_of_particles) ;
end
new_particles = new_particles*(vectors') ;
function [c,SqrtVariance,Weights] = mykmeans(x,g,init,cod)
function [c,SqrtVariance,Weights] = mykmeans(x,g,init,cod)
% Copyright (C) 2013 Dynare Team
%
......@@ -20,34 +20,34 @@ function [c,SqrtVariance,Weights] = mykmeans(x,g,init,cod)
[n,m] = size(x) ;
indold = zeros(1,m) ;