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Update calling sequence of local_state_space_iteration_k for new Fortran version

remove-submodule^2
Sébastien Villemot 2022-02-04 11:29:31 +01:00
parent 2961cd3fd8
commit 1b5c02176b
9 changed files with 36 additions and 26 deletions
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9
nonlinear-filters/src/auxiliary_particle_filter.m
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@ -3,7 +3,7 @@ function [LIK,lik] = auxiliary_particle_filter(ReducedForm,Y,start,ParticleOptio
% Evaluates the likelihood of a nonlinear model with the auxiliary particle filter
% allowing eventually resampling.
%
% Copyright (C) 2011-2019 Dynare Team
% Copyright (C) 2011-2022 Dynare Team
%
% This file is part of Dynare (particles module).
%
@ -42,6 +42,7 @@ number_of_particles = ParticleOptions.number_of_particles;
if ReducedForm.use_k_order_solver
dr = ReducedForm.dr;
udr = ReducedForm.udr;
else
% Set local state space model (first order approximation).
ghx = ReducedForm.ghx;
@ -96,7 +97,7 @@ for t=1:sample_size
if ReducedForm.use_k_order_solver
tmp = 0;
for i=1:size(nodes)
tmp = tmp + nodes_weights(i)*local_state_space_iteration_k(yhat, nodes(i,:)'*ones(1,number_of_particles), dr, Model, DynareOptions);
tmp = tmp + nodes_weights(i)*local_state_space_iteration_k(yhat, nodes(i,:)'*ones(1,number_of_particles), dr, Model, DynareOptions, udr);
end
else
tmp = 0;
@ -121,7 +122,7 @@ for t=1:sample_size
StateVectors_ = tmp_(mf0,:);
else
if ReducedForm.use_k_order_solver
tmp = local_state_space_iteration_k(yhat, epsilon, dr, Model, DynareOptions);
tmp = local_state_space_iteration_k(yhat, epsilon, dr, Model, DynareOptions, udr);
else
tmp = local_state_space_iteration_2(yhat, epsilon, ghx, ghu, constant, ghxx, ghuu, ghxu, ThreadsOptions.local_state_space_iteration_2);
end
@ -144,4 +145,4 @@ for t=1:sample_size
end
end
LIK = -sum(lik(start:end));
LIK = -sum(lik(start:end));

5
nonlinear-filters/src/conditional_filter_proposal.m
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@ -22,7 +22,7 @@ function [ProposalStateVector, Weights, flag] = conditional_filter_proposal(Redu
% - Weights
% - flag
% Copyright © 2012-2020 Dynare Team
% Copyright © 2012-2022 Dynare Team
%
% This file is part of Dynare.
%
@ -43,6 +43,7 @@ flag = false;
if ReducedForm.use_k_order_solver
dr = ReducedForm.dr;
udr = ReducedForm.udr;
else
% Set local state space model (first-order approximation).
ghx = ReducedForm.ghx;
@ -79,7 +80,7 @@ epsilon = Q_lower_triangular_cholesky*nodes';
yhat = repmat(StateVectors-state_variables_steady_state, 1, size(epsilon, 2));
if ReducedForm.use_k_order_solver
tmp = local_state_space_iteration_k(yhat, epsilon, dr, Model, DynareOptions);
tmp = local_state_space_iteration_k(yhat, epsilon, dr, Model, DynareOptions, udr);
else
tmp = local_state_space_iteration_2(yhat, epsilon, ghx, ghu, constant, ghxx, ghuu, ghxu, ThreadsOptions.local_state_space_iteration_2);
end

7
nonlinear-filters/src/gaussian_filter_bank.m
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@ -22,7 +22,7 @@ function [PredictedStateMean, PredictedStateVarianceSquareRoot, StateVectorMean,
% NOTES
% The vector "lik" is used to evaluate the jacobian of the likelihood.
% Copyright (C) 2009-2017 Dynare Team
% Copyright (C) 2009-2022 Dynare Team
%
% This file is part of Dynare.
%
@ -41,6 +41,7 @@ function [PredictedStateMean, PredictedStateVarianceSquareRoot, StateVectorMean,
if ReducedForm.use_k_order_solver
dr = ReducedForm.dr;
udr = ReducedForm.udr;
else
% Set local state space model (first-order approximation).
ghx = ReducedForm.ghx;
@ -81,7 +82,7 @@ StateVectors = sigma_points(1:number_of_state_variables,:);
epsilon = sigma_points(number_of_state_variables+1:number_of_state_variables+number_of_structural_innovations,:);
yhat = bsxfun(@minus, StateVectors, state_variables_steady_state);
if ReducedForm.use_k_order_solver
tmp = local_state_space_iteration_k(yhat, epsilon, dr, Model, DynareOptions);
tmp = local_state_space_iteration_k(yhat, epsilon, dr, Model, DynareOptions, udr);
else
tmp = local_state_space_iteration_2(yhat, epsilon, ghx, ghu, constant, ghxx, ghuu, ghxu, ThreadsOptions.local_state_space_iteration_2);
end
@ -116,4 +117,4 @@ else
StateVectorVariance = PredictedStateVariance - KalmanFilterGain*PredictedObservedVariance*KalmanFilterGain';
StateVectorVariance = .5*(StateVectorVariance+StateVectorVariance');
StateVectorVarianceSquareRoot = reduced_rank_cholesky(StateVectorVariance)';
end
end

7
nonlinear-filters/src/gaussian_mixture_filter_bank.m
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@ -24,7 +24,7 @@ function [StateMuPrior,StateSqrtPPrior,StateWeightsPrior,StateMuPost,StateSqrtPP
% NOTES
% The vector "lik" is used to evaluate the jacobian of the likelihood.
% Copyright (C) 2009-2019 Dynare Team
% Copyright (C) 2009-2022 Dynare Team
%
% This file is part of Dynare.
%
@ -43,6 +43,7 @@ function [StateMuPrior,StateSqrtPPrior,StateWeightsPrior,StateMuPost,StateSqrtPP
if ReducedForm.use_k_order_solver
dr = ReducedForm.dr;
udr = ReducedForm.udr;
else
% Set local state space model (first-order approximation).
ghx = ReducedForm.ghx;
@ -77,7 +78,7 @@ epsilon = bsxfun(@plus, StructuralShocksSqrtP*nodes3(:,number_of_state_variables
StateVectors = bsxfun(@plus, StateSqrtP*nodes3(:,1:number_of_state_variables)', StateMu);
yhat = bsxfun(@minus, StateVectors, state_variables_steady_state);
if ReducedForm.use_k_order_solver
tmp = local_state_space_iteration_k(yhat, epsilon, dr, Model, DynareOptions);
tmp = local_state_space_iteration_k(yhat, epsilon, dr, Model, DynareOptions, udr);
else
tmp = local_state_space_iteration_2(yhat, epsilon, ghx, ghu, constant, ghxx, ghuu, ghxu, ThreadsOptions.local_state_space_iteration_2);
end
@ -123,4 +124,4 @@ StateSqrtPPrior = reduced_rank_cholesky(PredictedStateVariance)';
StateWeightsPrior = StateWeights*StructuralShocksWeights;
StateMuPost = StateVectorMean;
StateSqrtPPost = StateVectorVarianceSquareRoot;
StateWeightsPost = StateWeightsPrior*ObservationShocksWeights*data_lik_GM_g;
StateWeightsPost = StateWeightsPrior*ObservationShocksWeights*data_lik_GM_g;

7
nonlinear-filters/src/measurement_equations.m
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@ -1,6 +1,6 @@
function measure = measurement_equations(StateVectors,ReducedForm,ThreadsOptions, DynareOptions, Model)
% Copyright (C) 2013-2019 Dynare Team
% Copyright (C) 2013-2022 Dynare Team
%
% This file is part of Dynare.
%
@ -20,6 +20,7 @@ function measure = measurement_equations(StateVectors,ReducedForm,ThreadsOptions
mf1 = ReducedForm.mf1;
if ReducedForm.use_k_order_solver
dr = ReducedForm.dr;
udr = ReducedForm.udr;
else
ghx = ReducedForm.ghx(mf1,:);
ghu = ReducedForm.ghu(mf1,:);
@ -32,8 +33,8 @@ state_variables_steady_state = ReducedForm.state_variables_steady_state;
number_of_structural_innovations = length(ReducedForm.Q);
yhat = bsxfun(@minus, StateVectors, state_variables_steady_state);
if ReducedForm.use_k_order_solver
tmp = local_state_space_iteration_k(yhat, zeros(number_of_structural_innovations, size(yhat,2)), dr, Model, DynareOptions);
tmp = local_state_space_iteration_k(yhat, zeros(number_of_structural_innovations, size(yhat,2)), dr, Model, DynareOptions, udr);
measure = tmp(mf1,:);
else
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);
end
end

7
nonlinear-filters/src/nonlinear_kalman_filter.m
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@ -32,7 +32,7 @@ function [LIK,lik] = nonlinear_kalman_filter(ReducedForm, Y, start, ParticleOpti
% NOTES
% The vector "lik" is used to evaluate the jacobian of the likelihood.
% Copyright (C) 2009-2019 Dynare Team
% Copyright (C) 2009-2022 Dynare Team
%
% This file is part of Dynare.
%
@ -56,6 +56,7 @@ end
if ReducedForm.use_k_order_solver
dr = ReducedForm.dr;
udr = ReducedForm.udr;
else
% Set local state space model (first-order approximation).
ghx = ReducedForm.ghx;
@ -116,7 +117,7 @@ for t=1:sample_size
epsilon = sigma_points(number_of_state_variables+1:number_of_state_variables+number_of_structural_innovations,:);
yhat = bsxfun(@minus,StateVectors,state_variables_steady_state);
if ReducedForm.use_k_order_solver
tmp = local_state_space_iteration_k(yhat, epsilon, dr, Model, DynareOptions);
tmp = local_state_space_iteration_k(yhat, epsilon, dr, Model, DynareOptions, udr);
else
tmp = local_state_space_iteration_2(yhat, epsilon, ghx, ghu, constant, ghxx, ghuu, ghxu, ThreadsOptions.local_state_space_iteration_2);
end
@ -161,4 +162,4 @@ for t=1:sample_size
lik(t) = log( sum(probability2(Y(:,t),H_lower_triangular_cholesky,tmp(mf1,:)).*weights,1) ) ;
end
LIK = -sum(lik(start:end));
LIK = -sum(lik(start:end));

8
nonlinear-filters/src/online_auxiliary_filter.m
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@ -21,7 +21,7 @@ function [pmean, pmode, pmedian, pstdev, p025, p975, covariance] = online_auxili
% - p975 [double] n×1 vector, 97.5 percent of the particles are below p975(i) for i=1,…,n.
% - covariance [double] n×n matrix, covariance of the particles at the end of the sample.
% Copyright © 2013-2021 Dynare Team
% Copyright © 2013-2022 Dynare Team
%
% This file is part of Dynare.
%
@ -125,6 +125,7 @@ for t=1:sample_size
% Set local state space model (second-order approximation).
if ReducedForm.use_k_order_solver
dr = ReducedForm.dr;
udr = ReducedForm.udr;
else
constant = ReducedForm.constant;
% Set local state space model (first-order approximation).
@ -138,7 +139,7 @@ for t=1:sample_size
% particle likelihood contribution
yhat = bsxfun(@minus, StateVectors(:,i), state_variables_steady_state);
if ReducedForm.use_k_order_solver
tmp = local_state_space_iteration_k(yhat, zeros(number_of_structural_innovations, 1), dr, Model, DynareOptions);
tmp = local_state_space_iteration_k(yhat, zeros(number_of_structural_innovations, 1), dr, Model, DynareOptions, udr);
else
if pruning
yhat_ = bsxfun(@minus,StateVectors_(:,i),state_variables_steady_state);
@ -181,6 +182,7 @@ for t=1:sample_size
% Set local state space model (second order approximation).
if ReducedForm.use_k_order_solver
dr = ReducedForm.dr;
udr = ReducedForm.udr;
else
constant = ReducedForm.constant;
% Set local state space model (first-order approximation).
@ -196,7 +198,7 @@ for t=1:sample_size
% compute particles likelihood contribution
yhat = bsxfun(@minus,StateVectors(:,i), state_variables_steady_state);
if ReducedForm.use_k_order_solver
tmp = local_state_space_iteration_k(yhat, epsilon, dr, Model, DynareOptions);
tmp = local_state_space_iteration_k(yhat, epsilon, dr, Model, DynareOptions, udr);
else
if pruning
yhat_ = bsxfun(@minus,StateVectors_(:,i), state_variables_steady_state);

7
nonlinear-filters/src/sequential_importance_particle_filter.m
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@ -2,7 +2,7 @@ function [LIK,lik] = sequential_importance_particle_filter(ReducedForm,Y,start,P
% Evaluates the likelihood of a nonlinear model with a particle filter (optionally with resampling).
% Copyright (C) 2011-2015 Dynare Team
% Copyright (C) 2011-2022 Dynare Team
%
% This file is part of Dynare (particles module).
%
@ -51,6 +51,7 @@ end
if ReducedForm.use_k_order_solver
dr = ReducedForm.dr;
udr = ReducedForm.udr;
else
% Set local state space model (first order approximation).
ghx = ReducedForm.ghx;
@ -106,7 +107,7 @@ for t=1:sample_size
[tmp, tmp_] = local_state_space_iteration_2(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,ghxu,yhat_,steadystate,ThreadsOptions.local_state_space_iteration_2);
else
if ReducedForm.use_k_order_solver
tmp = local_state_space_iteration_k(yhat, epsilon, dr, Model, DynareOptions);
tmp = local_state_space_iteration_k(yhat, epsilon, dr, Model, DynareOptions, udr);
else
tmp = local_state_space_iteration_2(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,ghxu,ThreadsOptions.local_state_space_iteration_2);
end
@ -143,4 +144,4 @@ for t=1:sample_size
end
end
LIK = -sum(lik(start:end));
LIK = -sum(lik(start:end));

5
nonlinear-filters/src/solve_model_for_online_filter.m
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@ -20,7 +20,7 @@ function [info, Model, DynareOptions, DynareResults, ReducedForm] = ...
% - DynareResults [struct] Dynare results (oo_).
% - ReducedForm [struct] Reduced form model.
% Copyright (C) 2013-2019 Dynare Team
% Copyright (C) 2013-2022 Dynare Team
%
% This file is part of Dynare.
%
@ -164,6 +164,7 @@ if nargout>4
elseif DynareOptions.order>=3
ReducedForm.use_k_order_solver = true;
ReducedForm.dr = dr;
ReducedForm.udr = folded_to_unfolded_dr(dr, Model, DynareOptions);
else
n_states=size(dr.ghx,2);
n_shocks=size(dr.ghu,2);
@ -210,4 +211,4 @@ if setinitialcondition
end
ReducedForm.StateVectorMean = StateVectorMean;
ReducedForm.StateVectorVariance = StateVectorVariance;
end
end