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Fixed indentation.

rm-particles^2
Stéphane Adjemian (Charybdis) 2017-05-18 23:59:10 +02:00
parent 823d947484
commit 4921000f0a
26 changed files with 317 additions and 326 deletions
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2
src/auxiliary_initialization.m
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@ -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,:));

10
src/auxiliary_particle_filter.m
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@ -1,6 +1,6 @@
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

14
src/conditional_filter_proposal.m
View File

@ -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

50
src/conditional_particle_filter.m
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@ -1,24 +1,24 @@
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)';

55
src/fit_gaussian_mixture.m
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@ -1,4 +1,4 @@
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

14
src/gaussian_densities.m
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@ -1,6 +1,6 @@
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 ;

22
src/gaussian_filter.m
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@ -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) ;

4
src/gaussian_filter_bank.m
View File

@ -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

17
src/gaussian_mixture_densities.m
View File

@ -1,8 +1,8 @@
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 ;

58
src/gaussian_mixture_filter.m
View File

@ -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 ;

20
src/gaussian_mixture_filter_bank.m
View File

@ -1,12 +1,12 @@
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

11
src/importance_sampling.m
View File

@ -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);

5
src/measurement_equations.m
View File

@ -1,4 +1,4 @@
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);

6
src/multivariate_smooth_resampling.m
View File

@ -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') ;

56
src/mykmeans.m
View File

@ -1,4 +1,4 @@
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) ;
if cod==0
d = transpose(sum(bsxfun(@power,bsxfun(@minus,x,mean(x)),2)));
d = sortrows( [transpose(1:m) d],2) ;
d = d((1+(0:1:g-1))*m/g,1) ;
c = x(:,d);
d = transpose(sum(bsxfun(@power,bsxfun(@minus,x,mean(x)),2)));
d = sortrows( [transpose(1:m) d],2) ;
d = d((1+(0:1:g-1))*m/g,1) ;
c = x(:,d);
else
c = init ;
end
for iter=1:300
dist = zeros(g,m) ;
for i=1:g
dist(i,:) = sum(bsxfun(@power,bsxfun(@minus,x,c(:,i)),2));
end
[rien,ind] = min(dist) ;
if isequal(ind,indold)
break ;
end
indold = ind ;
for i=1:g
lin = bsxfun(@eq,ind,i.*ones(1,m)) ;
h = x(:,lin) ;
c(:,i) = mean(h,2) ;
end
c = init ;
end
SqrtVariance = zeros(n,n,g) ;
Weights = zeros(1,g) ;
for iter=1:300
dist = zeros(g,m) ;
for i=1:g
dist(i,:) = sum(bsxfun(@power,bsxfun(@minus,x,c(:,i)),2));
end
[rien,ind] = min(dist) ;
if isequal(ind,indold)
break ;
end
indold = ind ;
for i=1:g
lin = bsxfun(@eq,ind,i.*ones(1,m)) ;
h = x(:,lin) ;
c(:,i) = mean(h,2) ;
end
end
SqrtVariance = zeros(n,n,g) ;
Weights = zeros(1,g) ;
for i=1:g
temp = x(:,bsxfun(@eq,ind,i*ones(1,m))) ;
u = bsxfun(@minus,temp,mean(temp,2)); %temp-mean(temp,1)' ;
SqrtVariance(:,:,i) = chol( (u*u')/size(temp,2) )' ;
Weights(i) = size(temp,2)/m ;
temp = x(:,bsxfun(@eq,ind,i*ones(1,m))) ;
u = bsxfun(@minus,temp,mean(temp,2)); %temp-mean(temp,1)' ;
SqrtVariance(:,:,i) = chol( (u*u')/size(temp,2) )' ;
Weights(i) = size(temp,2)/m ;
end

18
src/nonlinear_kalman_filter.m
View File

@ -10,9 +10,9 @@ function [LIK,lik] = nonlinear_kalman_filter(ReducedForm, Y, start, ParticleOpti
% from the resulting nodes/particles and allows to generate new distributions at the
% following observation.
% Pros: The use of nodes is much faster than Monte-Carlo Gaussian particle and standard particles
% filters since it treats a lesser number of particles.
% Cons: 1. Application a linear projection formulae in a nonlinear context.
% 2. Parameter estimations may be biaised if the model is truly non-gaussian since predictive and
% filters since it treats a lesser number of particles.
% Cons: 1. Application a linear projection formulae in a nonlinear context.
% 2. Parameter estimations may be biaised if the model is truly non-gaussian since predictive and
% filtered densities are unimodal.
%
% INPUTS
@ -47,7 +47,7 @@ function [LIK,lik] = nonlinear_kalman_filter(ReducedForm, Y, start, ParticleOpti
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
persistent init_flag mf0 mf1 nodes weights weights_c
persistent init_flag mf0 mf1 nodes weights weights_c
persistent sample_size number_of_state_variables number_of_observed_variables number_of_structural_innovations
% Set default
@ -64,8 +64,8 @@ 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
@ -100,12 +100,12 @@ end
if ParticleOptions.distribution_approximation.montecarlo
set_dynare_seed('default');
end
end
% Get covariance matrices
H = ReducedForm.H;
H_lower_triangular_cholesky = chol(H)' ;
Q_lower_triangular_cholesky = chol(ReducedForm.Q)';
H_lower_triangular_cholesky = chol(H)' ;
Q_lower_triangular_cholesky = chol(ReducedForm.Q)';
% Get initial condition for the state vector.
StateVectorMean = ReducedForm.StateVectorMean;

93
src/online_auxiliary_filter.m
View File

@ -1,5 +1,5 @@
function [xparam,std_param,lb_95,ub_95,median_param] = online_auxiliary_filter(xparam1,DynareDataset,dataset_info,DynareOptions,Model,EstimatedParameters,BayesInfo,bounds,DynareResults)
% Liu & West particle filter = auxiliary particle filter including Liu & West filter on parameters.
%
% INPUTS
@ -47,8 +47,8 @@ second_resample = 0 ;
variance_update = 1 ;
% initialization of state particles
[ys,trend_coeff,exit_flag,info,Model,DynareOptions,BayesInfo,DynareResults,ReducedForm] = ...
solve_model_for_online_filter(1,xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults) ;
[ys,trend_coeff,exit_flag,info,Model,DynareOptions,BayesInfo,DynareResults,ReducedForm] = ...
solve_model_for_online_filter(1,xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults) ;
% Set persistent variables.
if isempty(init_flag)
@ -61,11 +61,11 @@ if isempty(init_flag)
number_of_observed_variables = length(mf1);
number_of_structural_innovations = length(ReducedForm.Q);
liu_west_delta = DynareOptions.particle.liu_west_delta ;
start_param = xparam1 ;
start_param = xparam1 ;
init_flag = 1;
end
% Get initial conditions for the state particles
% Get initial conditions for the state particles
StateVectorMean = ReducedForm.StateVectorMean;
StateVectorVarianceSquareRoot = chol(ReducedForm.StateVectorVariance)';
state_variance_rank = size(StateVectorVarianceSquareRoot,2);
@ -73,13 +73,13 @@ StateVectors = bsxfun(@plus,StateVectorVarianceSquareRoot*randn(state_variance_r
if pruning
StateVectors_ = StateVectors;
end
% parameters for the Liu & West filter
% parameters for the Liu & West filter
h_square = (3*liu_west_delta-1)/(2*liu_west_delta) ;
h_square = 1-h_square*h_square ;
small_a = sqrt(1-h_square) ;
% Initialization of parameter particles
% Initialization of parameter particles
xparam = zeros(number_of_parameters,number_of_particles) ;
stderr = sqrt(bsxfun(@power,bounds.ub-bounds.lb,2)/12)/100 ;
stderr = sqrt(bsxfun(@power,bounds.ub-bounds.lb,2)/12)/50 ;
@ -107,14 +107,14 @@ std_xparam = zeros(number_of_parameters,sample_size) ;
lb95_xparam = zeros(number_of_parameters,sample_size) ;
ub95_xparam = zeros(number_of_parameters,sample_size) ;
%% The Online filter
%% The Online filter
for t=1:sample_size
if t>1
fprintf('\nSubsample with %s first observations.\n\n', int2str(t))
else
fprintf('\nSubsample with only the first observation.\n\n', int2str(t))
end
% Moments of parameters particles distribution
% Moments of parameters particles distribution
m_bar = xparam*(weights') ;
temp = bsxfun(@minus,xparam,m_bar) ;
sigma_bar = (bsxfun(@times,weights,temp))*(temp') ;
@ -124,8 +124,8 @@ for t=1:sample_size
% Prediction (without shocks)
tau_tilde = zeros(1,number_of_particles) ;
for i=1:number_of_particles
% model resolution
[ys,trend_coeff,exit_flag,info,Model,DynareOptions,BayesInfo,DynareResults,ReducedForm] = ...
% model resolution
[ys,trend_coeff,exit_flag,info,Model,DynareOptions,BayesInfo,DynareResults,ReducedForm] = ...
solve_model_for_online_filter(t,xparam(:,i),DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults) ;
steadystate = ReducedForm.steadystate;
state_variables_steady_state = ReducedForm.state_variables_steady_state;
@ -136,7 +136,7 @@ for t=1:sample_size
ghxx = ReducedForm.ghxx;
ghuu = ReducedForm.ghuu;
ghxu = ReducedForm.ghxu;
% particle likelihood contribution
% particle likelihood contribution
yhat = bsxfun(@minus,StateVectors(:,i),state_variables_steady_state);
if pruning
yhat_ = bsxfun(@minus,StateVectors_(:,i),state_variables_steady_state);
@ -151,7 +151,7 @@ for t=1:sample_size
tau_tilde(i) = weights(i).*(tpdf(z,3*ones(size(z)))+1e-99) ;
%tau_tilde(i) = weights(i).*exp(-.5*(const_lik+log(det(ReducedForm.H))+sum(PredictionError.*(ReducedForm.H\PredictionError),1))) ;
end
% particles selection
% particles selection
tau_tilde = tau_tilde/sum(tau_tilde) ;
indx = resample(0,tau_tilde',DynareOptions.particle);
StateVectors = StateVectors(:,indx) ;
@ -160,7 +160,7 @@ for t=1:sample_size
end
xparam = bsxfun(@plus,(1-small_a).*m_bar,small_a.*xparam(:,indx)) ;
w_stage1 = weights(indx)./tau_tilde(indx) ;
% draw in the new distributions
% draw in the new distributions
wtilde = zeros(1,number_of_particles) ;
i = 1 ;
while i<=number_of_particles
@ -179,9 +179,9 @@ for t=1:sample_size
ghxx = ReducedForm.ghxx;
ghuu = ReducedForm.ghuu;
ghxu = ReducedForm.ghxu;
% Get covariance matrices and structural shocks
% Get covariance matrices and structural shocks
epsilon = chol(ReducedForm.Q)'*randn(number_of_structural_innovations,1) ;
% compute particles likelihood contribution
% compute particles likelihood contribution
yhat = bsxfun(@minus,StateVectors(:,i),state_variables_steady_state);
if pruning
yhat_ = bsxfun(@minus,StateVectors_(:,i),state_variables_steady_state);
@ -196,13 +196,13 @@ for t=1:sample_size
i = i+1 ;
end
end
% normalization
% normalization
weights = wtilde/sum(wtilde);
if (variance_update==1) && (neff(weights)<DynareOptions.particle.resampling.threshold*sample_size)
variance_update = 0 ;
end
% final resampling (not advised)
if second_resample==1
if second_resample==1
indx = resample(0,weights,DynareOptions.particle);
StateVectors = StateVectors(:,indx) ;
if pruning
@ -212,13 +212,13 @@ for t=1:sample_size
weights = ones(1,number_of_particles)/number_of_particles ;
mean_xparam(:,t) = mean(xparam,2);
mat_var_cov = bsxfun(@minus,xparam,mean_xparam(:,t)) ;
mat_var_cov = (mat_var_cov*mat_var_cov')/(number_of_particles-1) ;
mat_var_cov = (mat_var_cov*mat_var_cov')/(number_of_particles-1) ;
std_xparam(:,t) = sqrt(diag(mat_var_cov)) ;
for i=1:number_of_parameters
temp = sortrows(xparam(i,:)') ;
lb95_xparam(i,t) = temp(0.025*number_of_particles) ;
median_xparam(i,t) = temp(0.5*number_of_particles) ;
ub95_xparam(i,t) = temp(0.975*number_of_particles) ;
temp = sortrows(xparam(i,:)') ;
lb95_xparam(i,t) = temp(0.025*number_of_particles) ;
median_xparam(i,t) = temp(0.5*number_of_particles) ;
ub95_xparam(i,t) = temp(0.975*number_of_particles) ;
end
end
if second_resample==0
@ -227,25 +227,25 @@ for t=1:sample_size
mat_var_cov = mat_var_cov*(bsxfun(@times,mat_var_cov,weights)') ;
std_xparam(:,t) = sqrt(diag(mat_var_cov)) ;
for i=1:number_of_parameters
temp = sortrows([xparam(i,:)' weights'],1) ;
cumulated_weights = cumsum(temp(:,2)) ;
pass1=1 ;
pass2=1 ;
pass3=1 ;
for j=1:number_of_particles
if cumulated_weights(j)>=0.025 && pass1==1
lb95_xparam(i,t) = temp(j,1) ;
pass1 = 2 ;
end
if cumulated_weights(j)>=0.5 && pass2==1
median_xparam(i,t) = temp(j,1) ;
pass2 = 2 ;
end
if cumulated_weights(j)>=0.975 && pass3==1
ub95_xparam(i,t) = temp(j,1) ;
pass3 = 2 ;
end
end
temp = sortrows([xparam(i,:)' weights'],1) ;
cumulated_weights = cumsum(temp(:,2)) ;
pass1=1 ;
pass2=1 ;
pass3=1 ;
for j=1:number_of_particles
if cumulated_weights(j)>=0.025 && pass1==1
lb95_xparam(i,t) = temp(j,1) ;
pass1 = 2 ;
end
if cumulated_weights(j)>=0.5 && pass2==1
median_xparam(i,t) = temp(j,1) ;
pass2 = 2 ;
end
if cumulated_weights(j)>=0.975 && pass3==1
ub95_xparam(i,t) = temp(j,1) ;
pass3 = 2 ;
end
end
end
end
str = sprintf(' Lower Bound (95%%) \t Mean \t\t\t Upper Bound (95%%)');
@ -262,7 +262,7 @@ lb_95 = lb95_xparam(:,sample_size) ;
ub_95 = ub95_xparam(:,sample_size) ;
median_param = median_xparam(:,sample_size) ;
%% Plot parameters trajectory
%% Plot parameters trajectory
TeX = DynareOptions.TeX;
[nbplt,nr,nc,lr,lc,nstar] = pltorg(number_of_parameters);
@ -322,7 +322,7 @@ end
%% Plot Parameter Densities
number_of_grid_points = 2^9; % 2^9 = 512 !... Must be a power of two.
bandwidth = 0; % Rule of thumb optimal bandwidth parameter.
kernel_function = 'gaussian'; % Gaussian kernel for Fast Fourier Transform approximation.
kernel_function = 'gaussian'; % Gaussian kernel for Fast Fourier Transform approximation.
for plt = 1:nbplt,
if TeX
NAMES = [];
@ -366,5 +366,4 @@ for plt = 1:nbplt,
fprintf(fidTeX,'\\end{figure}\n');
fprintf(fidTeX,' \n');
end
end
end

24
src/probability.m
View File

@ -17,23 +17,23 @@ function [prior,likelihood,C,posterior] = probability(mu,sqrtP,prior,X)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
[dim,nov] = size(X);
[dim,nov] = size(X);
M = size(mu,2) ;
if nargout>1
likelihood = zeros(M,nov);
normfact = (2*pi)^(dim/2);
for k=1:M
XX = bsxfun(@minus,X,mu(:,k));
S = sqrtP(:,:,k);
foo = S \ XX;
likelihood(k,:) = exp(-0.5*sum(foo.*foo, 1))/abs((normfact*prod(diag(S))));
end
likelihood = zeros(M,nov);
normfact = (2*pi)^(dim/2);
for k=1:M
XX = bsxfun(@minus,X,mu(:,k));
S = sqrtP(:,:,k);
foo = S \ XX;
likelihood(k,:) = exp(-0.5*sum(foo.*foo, 1))/abs((normfact*prod(diag(S))));
end
end
likelihood = likelihood + 1e-99;
if nargout>2
C = prior*likelihood + 1e-99;
C = prior*likelihood + 1e-99;
end
if nargout>3
posterior = bsxfun(@rdivide,bsxfun(@times,prior',likelihood),C) + 1e-99 ;
posterior = bsxfun(@rdivide,posterior,sum(posterior,1));
posterior = bsxfun(@rdivide,bsxfun(@times,prior',likelihood),C) + 1e-99 ;
posterior = bsxfun(@rdivide,posterior,sum(posterior,1));
end

12
src/probability2.m
View File

@ -1,7 +1,7 @@
function [density] = probability2(mu,S,X)
function [density] = probability2(mu,S,X)
%
% Multivariate gaussian density
%
%
% INPUTS
% n [integer] scalar, number of variables.
%
@ -10,10 +10,10 @@ function [density] = probability2(mu,S,X)
% weigths [double] associated weigths
%
% REFERENCES
%
%
%
% NOTES
%
%
% Copyright (C) 2009-2012 Dynare Team
%
% This file is part of Dynare.
@ -31,7 +31,7 @@ function [density] = probability2(mu,S,X)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
dim = size(X,1) ;
normfact = bsxfun(@power,(2*pi),(dim/2)) ;
dim = size(X,1) ;
normfact = bsxfun(@power,(2*pi),(dim/2)) ;
foo = S\(bsxfun(@minus,X,mu)) ;
density = exp(-0.5*sum(foo.*foo)')./abs((normfact*prod(diag(S)))) + 1e-99 ;

24
src/probability3.m
View File

@ -17,23 +17,23 @@ function [prior,likelihood,C,posterior] = probability3(mu,sqrtP,prior,X,X_weight
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
[dim,nov] = size(X);
[dim,nov] = size(X);
M = size(mu,2) ;
if nargout>1
likelihood = zeros(M,nov);
normfact = (2*pi)^(dim/2);
for k=1:M
XX = bsxfun(@minus,X,mu(:,k));
S = sqrtP(:,:,k);
foo = S \ XX;
likelihood(k,:) = exp(-0.5*sum(foo.*foo, 1))/abs((normfact*prod(diag(S))));
end
likelihood = zeros(M,nov);
normfact = (2*pi)^(dim/2);
for k=1:M
XX = bsxfun(@minus,X,mu(:,k));
S = sqrtP(:,:,k);
foo = S \ XX;
likelihood(k,:) = exp(-0.5*sum(foo.*foo, 1))/abs((normfact*prod(diag(S))));
end
end
wlikelihood = bsxfun(@times,X_weights,likelihood) + 1e-99;
if nargout>2
C = prior*wlikelihood + 1e-99;
C = prior*wlikelihood + 1e-99;
end
if nargout>3
posterior = bsxfun(@rdivide,bsxfun(@times,prior',wlikelihood),C) + 1e-99 ;
posterior = bsxfun(@rdivide,posterior,sum(posterior,1));
posterior = bsxfun(@rdivide,bsxfun(@times,prior',wlikelihood),C) + 1e-99 ;
posterior = bsxfun(@rdivide,posterior,sum(posterior,1));
end

40
src/residual_resampling.m
View File

@ -76,33 +76,33 @@ iWEIGHTS = fix(WEIGHTS);
number_of_trials = number_of_particles-sum(iWEIGHTS);
if number_of_trials
WEIGHTS = (WEIGHTS-iWEIGHTS)/number_of_trials;
EmpiricalCDF = cumsum(WEIGHTS);
if kitagawa_resampling
u = (transpose(1:number_of_trials)-1+noise(:))/number_of_trials;
else
u = fliplr(cumprod(noise(1:number_of_trials).^(1./(number_of_trials:-1:1))));
end
j=1;
for i=1:number_of_trials
while (u(i)>EmpiricalCDF(j))
j=j+1;
WEIGHTS = (WEIGHTS-iWEIGHTS)/number_of_trials;
EmpiricalCDF = cumsum(WEIGHTS);
if kitagawa_resampling
u = (transpose(1:number_of_trials)-1+noise(:))/number_of_trials;
else
u = fliplr(cumprod(noise(1:number_of_trials).^(1./(number_of_trials:-1:1))));
end
iWEIGHTS(j)=iWEIGHTS(j)+1;
if kitagawa_resampling==0
j=1;
j=1;
for i=1:number_of_trials
while (u(i)>EmpiricalCDF(j))
j=j+1;
end
iWEIGHTS(j)=iWEIGHTS(j)+1;
if kitagawa_resampling==0
j=1;
end
end
end
end
k=1;
for i=1:number_of_particles
if (iWEIGHTS(i)>0)
for j=k:k+iWEIGHTS(i)-1
indx(j) = jndx(i);
if (iWEIGHTS(i)>0)
for j=k:k+iWEIGHTS(i)-1
indx(j) = jndx(i);
end
end
end
k = k + iWEIGHTS(i);
k = k + iWEIGHTS(i);
end
if particles==0

54
src/solve_model_for_online_filter.m
View File

@ -185,18 +185,18 @@ if EstimatedParameters.ncx
Q(k2,k1) = Q(k1,k2);
end
% Try to compute the cholesky decomposition of Q (possible iff Q is positive definite)
% [CholQ,testQ] = chol(Q);
% if testQ
% The variance-covariance matrix of the structural innovations is not definite positive. We have to compute the eigenvalues of this matrix in order to build the endogenous penalty.
% a = diag(eig(Q));
% k = find(a < 0);
% if k > 0
% fval = objective_function_penalty_base+sum(-a(k));
% exit_flag = 0;
% info = 43;
% return
% end
% end
% [CholQ,testQ] = chol(Q);
% if testQ
% The variance-covariance matrix of the structural innovations is not definite positive. We have to compute the eigenvalues of this matrix in order to build the endogenous penalty.
% a = diag(eig(Q));
% k = find(a < 0);
% if k > 0
% fval = objective_function_penalty_base+sum(-a(k));
% exit_flag = 0;
% info = 43;
% return
% end
% end
offset = offset+EstimatedParameters.ncx;
end
@ -210,18 +210,18 @@ if EstimatedParameters.ncn
H(k2,k1) = H(k1,k2);
end
% Try to compute the cholesky decomposition of H (possible iff H is positive definite)
% [CholH,testH] = chol(H);
% if testH
% The variance-covariance matrix of the measurement errors is not definite positive. We have to compute the eigenvalues of this matrix in order to build the endogenous penalty.
% a = diag(eig(H));
% k = find(a < 0);
% if k > 0
% fval = objective_function_penalty_base+sum(-a(k));
% exit_flag = 0;
% info = 44;
% return
% end
% end
% [CholH,testH] = chol(H);
% if testH
% The variance-covariance matrix of the measurement errors is not definite positive. We have to compute the eigenvalues of this matrix in order to build the endogenous penalty.
% a = diag(eig(H));
% k = find(a < 0);
% if k > 0
% fval = objective_function_penalty_base+sum(-a(k));
% exit_flag = 0;
% info = 44;
% return
% end
% end
offset = offset+EstimatedParameters.ncn;
end
@ -244,7 +244,7 @@ Model.H = H;
%disp(info)
if info(1) ~= 0
ReducedForm = 0 ;
ReducedForm = 0 ;
exit_flag = 55;
return
end
@ -312,7 +312,7 @@ if DynareOptions.order>1
ReducedForm.ghuu = dr.ghuu(restrict_variables_idx,:);
ReducedForm.ghxu = dr.ghxu(restrict_variables_idx,:);
ReducedForm.constant = ReducedForm.steadystate + .5*dr.ghs2(restrict_variables_idx);
else
else
ReducedForm.ghxx = zeros(size(restrict_variables_idx,1),size(dr.kstate,2));
ReducedForm.ghuu = zeros(size(restrict_variables_idx,1),size(dr.ghu,2));
ReducedForm.ghxu = zeros(size(restrict_variables_idx,1),size(dr.ghx,2));
@ -325,7 +325,7 @@ ReducedForm.mf0 = mf0;
ReducedForm.mf1 = mf1;
% Set initial condition for t=1
if observation_number==1
if observation_number==1
switch DynareOptions.particle.initialization
case 1% Initial state vector covariance is the ergodic variance associated to the first order Taylor-approximation of the model.
StateVectorMean = ReducedForm.constant(mf0);

10
src/spherical_radial_sigma_points.m
View File

@ -1,7 +1,7 @@
function [nodes,weights] = spherical_radial_sigma_points(n)
function [nodes,weights] = spherical_radial_sigma_points(n)
%
% Computes nodes and weigths from a third-degree spherical-radial cubature
% rule.
% Computes nodes and weigths from a third-degree spherical-radial cubature
% rule.
% INPUTS
% n [integer] scalar, number of variables.
%
@ -11,10 +11,10 @@ function [nodes,weights] = spherical_radial_sigma_points(n)
%
% REFERENCES
%
% Arasaratnam & Haykin 2008,2009.
% Arasaratnam & Haykin 2008,2009.
%
% NOTES
%
%
% Copyright (C) 2009-2012 Dynare Team
%
% This file is part of Dynare.

2
src/traditional_resampling.m
View File

@ -75,7 +75,7 @@ c = cumsum(weights);
% Draw a starting point.
if kitagawa_resampling
randvec = (transpose(1:number_of_particles)-1+noise(:))/number_of_particles ;
else
else
randvec = fliplr(cumprod(noise.^(1./(number_of_particles:-1:1))));
end

6
src/univariate_smooth_resampling.m
View File

@ -61,16 +61,16 @@ lambda_tilde = [ (.5*weights(1)) ;
lambda_bar = cumsum(lambda_tilde) ;
u = rand(1,1) ;
new_particles = zeros(number_of_new_particles,1) ;
rj = 0 ;
rj = 0 ;
i = 1 ;
j = 1 ;
while i<=number_of_new_particles
u_j = ( i-1 + u)/number_of_new_particles ;
while u_j>lambda_bar(j)
rj = j ;
rj = j ;
j = j+1 ;
end
if rj==0
if rj==0
new_particles(i) = particles(1) ;
elseif rj==M
new_particles(i) = particles(M) ;

16
src/unscented_sigma_points.m
View File

@ -1,6 +1,6 @@
function [nodes,W_m,W_c] = unscented_sigma_points(n,ParticleOptions)
function [nodes,W_m,W_c] = unscented_sigma_points(n,ParticleOptions)
%
% Computes nodes and weigths for a scaled unscented transform cubature
% Computes nodes and weigths for a scaled unscented transform cubature
% INPUTS
% n [integer] scalar, number of variables.
%
@ -10,10 +10,10 @@ function [nodes,W_m,W_c] = unscented_sigma_points(n,ParticleOptions)
%
% REFERENCES
%
%
%
%
% NOTES
%
%
% Copyright (C) 2009-2012 Dynare Team
%
% This file is part of Dynare.
@ -31,12 +31,10 @@ function [nodes,W_m,W_c] = unscented_sigma_points(n,ParticleOptions)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
lambda = (ParticleOptions.unscented.alpha^2)*(n+ParticleOptions.unscented.kappa) - n ;
lambda = (ParticleOptions.unscented.alpha^2)*(n+ParticleOptions.unscented.kappa) - n ;
nodes = [ zeros(n,1) ( sqrt(n+lambda).*([ eye(n) -eye(n)]) ) ]' ;
W_m = lambda/(n+lambda) ;
W_c = W_m + (1-ParticleOptions.unscented.alpha^2+ParticleOptions.unscented.beta) ;
temp = ones(2*n,1)/(2*(n+lambda)) ;
W_m = [W_m ; temp] ;
W_c = [W_c ; temp] ;
W_m = [W_m ; temp] ;
W_c = [W_c ; temp] ;