### Deleted trailing white spaces.

parent 859335b3
 ... ... @@ -101,7 +101,7 @@ function [fval,exit_flag,ys,trend_coeff,info,Model,DynareOptions,BayesInfo,Dynar %! @end deftypefn %@eod: % Copyright (C) 2010-2011 Dynare Team % Copyright (C) 2010, 2011, 2012 Dynare Team % % This file is part of Dynare. % ... ... @@ -197,8 +197,8 @@ if EstimatedParameters_.ncx 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. 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 ... ... @@ -212,7 +212,7 @@ if EstimatedParameters_.ncx end % Get the off-diagonal elements of the covariance matrix for the measurement errors. Test if H is positive definite. if EstimatedParameters_.ncn if EstimatedParameters_.ncn for i=1:EstimatedParameters_.ncn k1 = DynareOptions.lgyidx2varobs(EstimatedParameters_.corrn(i,1)); k2 = DynareOptions.lgyidx2varobs(EstimatedParameters_.corrn(i,2)); ... ... @@ -266,8 +266,8 @@ BayesInfo.mf = BayesInfo.mf1; % Define the deterministic linear trend of the measurement equation. if DynareOptions.noconstant constant = zeros(nvobs,1); else constant = zeros(nvobs,1); else if DynareOptions.loglinear constant = log(SteadyState(BayesInfo.mfys)); else ... ... @@ -332,7 +332,7 @@ ReducedForm.mf1 = mf1; % Set initial condition. switch DynareOptions.particle.initialization case 1% Initial state vector covariance is the ergodic variance associated to the first order Taylor-approximation of the model. case 1% Initial state vector covariance is the ergodic variance associated to the first order Taylor-approximation of the model. StateVectorMean = ReducedForm.constant(mf0); StateVectorVariance = lyapunov_symm(ReducedForm.ghx(mf0,:),ReducedForm.ghu(mf0,:)*ReducedForm.Q*ReducedForm.ghu(mf0,:)',1e-12,1e-12); case 2% Initial state vector covariance is a monte-carlo based estimate of the ergodic variance (consistent with a k-order Taylor-approximation of the model). ... ...
 ... ... @@ -5,7 +5,7 @@ function [LIK,lik] = sequential_importance_particle_filter(ReducedForm,Y,start,D %! @deftypefn {Function File} {@var{y}, @var{y_} =} sequential_importance_particle_filter (@var{ReducedForm},@var{Y}, @var{start}, @var{DynareOptions}) %! @anchor{particle/sequential_importance_particle_filter} %! @sp 1 %! Evaluates the likelihood of a nonlinear model with a particle filter (optionally with resampling). %! Evaluates the likelihood of a nonlinear model with a particle filter (optionally with resampling). %! %! @sp 2 %! @strong{Inputs} ... ... @@ -14,7 +14,7 @@ function [LIK,lik] = sequential_importance_particle_filter(ReducedForm,Y,start,D %! @item ReducedForm %! Structure describing the state space model (built in @ref{non_linear_dsge_likelihood}). %! @item Y %! p*smpl matrix of doubles (p is the number of observed variables), the (detrended) data. %! p*smpl matrix of doubles (p is the number of observed variables), the (detrended) data. %! @item start %! Integer scalar, likelihood evaluation starts at observation 'start'. %! @item DynareOptions ... ... @@ -38,7 +38,7 @@ function [LIK,lik] = sequential_importance_particle_filter(ReducedForm,Y,start,D %! @end deftypefn %@eod: % Copyright (C) 2011 Dynare Team % Copyright (C) 2011, 2012 Dynare Team % % This file is part of Dynare. % ... ... @@ -56,10 +56,10 @@ function [LIK,lik] = sequential_importance_particle_filter(ReducedForm,Y,start,D % along with Dynare. If not, see . % AUTHOR(S) frederic DOT karame AT univ DASH evry DOT fr % stephane DOT adjemian AT univ DASH lemans DOT fr % stephane DOT adjemian AT univ DASH lemans DOT fr persistent init_flag persistent mf0 mf1 persistent init_flag persistent mf0 mf1 persistent number_of_particles persistent sample_size number_of_observed_variables number_of_structural_innovations ... ... @@ -73,13 +73,13 @@ steadystate = ReducedForm.steadystate; constant = ReducedForm.constant; state_variables_steady_state = ReducedForm.state_variables_steady_state; % Set persistent variables. % Set persistent variables (if needed). if isempty(init_flag) mf0 = ReducedForm.mf0; mf1 = ReducedForm.mf1; sample_size = size(Y,2); number_of_observed_variables = length(mf1); number_of_structural_innovations = length(ReducedForm.Q); number_of_structural_innovations = length(ReducedForm.Q); number_of_particles = DynareOptions.particle.number_of_particles; init_flag = 1; end ... ... @@ -93,7 +93,7 @@ ghxx = ReducedForm.ghxx; ghuu = ReducedForm.ghuu; ghxu = ReducedForm.ghxu; % Get covariance matrices % Get covariance matrices. Q = ReducedForm.Q; H = ReducedForm.H; if isempty(H) ... ... @@ -111,7 +111,7 @@ stream=RandStream('mt19937ar','Seed',1); RandStream.setDefaultStream(stream); % Initialization of the likelihood. const_lik = log(2*pi)*number_of_observed_variables; const_lik = log(2*pi)*number_of_observed_variables; lik = NaN(sample_size,1); % Initialization of the weights across particles. ... ... @@ -138,7 +138,7 @@ for t=1:sample_size weights = ones(1,number_of_particles) ; elseif ~isempty(strmatch(DynareOptions.particle_filter.resampling,'none','exact')) StateVectors = tmp(mf0,:); weights = number_of_particles*weights ; weights = number_of_particles*weights; end end ... ...
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