removed oo_.smoother.integration_order
removed integration_order (d) from the output arguments of DsgeSmoother. It is still computed by a few Kalman smoother routines. I don't change them now as they need to be rewritten soon.time-shift
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c1b3627896
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f85049e9a6
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@ -1,4 +1,4 @@
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function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK,T,R,P,PK,d,decomp] = DsgeSmoother(xparam1,gend,Y,data_index,missing_value)
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function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK,T,R,P,PK,decomp] = DsgeSmoother(xparam1,gend,Y,data_index,missing_value)
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% Estimation of the smoothed variables and innovations.
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% Estimation of the smoothed variables and innovations.
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%
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%
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% INPUTS
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% INPUTS
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@ -21,8 +21,6 @@ function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK,T,R,P,PK,d,
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% matrices
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% matrices
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% PK: 4D array of k-step ahead forecast error variance
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% PK: 4D array of k-step ahead forecast error variance
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% matrices (meaningless for periods 1:d)
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% matrices (meaningless for periods 1:d)
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% d: number of periods where filter remains in diffuse part
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% (should be equal to the order of integration of the model)
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%
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%
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% ALGORITHM
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% ALGORITHM
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% Diffuse Kalman filter (Durbin and Koopman)
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% Diffuse Kalman filter (Durbin and Koopman)
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@ -60,7 +58,6 @@ T = [];
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R = [];
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R = [];
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P = [];
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P = [];
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PK = [];
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PK = [];
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d = [];
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decomp = [];
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decomp = [];
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nobs = size(options_.varobs,1);
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nobs = size(options_.varobs,1);
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smpl = size(Y,2);
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smpl = size(Y,2);
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@ -356,10 +356,9 @@ initial_estimation_checks(xparam1,gend,data,data_index,number_of_observations,no
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if options_.mode_compute == 0 && length(options_.mode_file) == 0
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if options_.mode_compute == 0 && length(options_.mode_file) == 0
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if options_.smoother == 1
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if options_.smoother == 1
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[atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,d,decomp] = DsgeSmoother(xparam1,gend,data,data_index,missing_value);
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[atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,decomp] = DsgeSmoother(xparam1,gend,data,data_index,missing_value);
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oo_.Smoother.SteadyState = ys;
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oo_.Smoother.SteadyState = ys;
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oo_.Smoother.TrendCoeffs = trend_coeff;
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oo_.Smoother.TrendCoeffs = trend_coeff;
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oo_.Smoother.integration_order = d;
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if options_.filter_covariance
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if options_.filter_covariance
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oo_.Smoother.variance = P;
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oo_.Smoother.variance = P;
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end
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end
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@ -1112,10 +1111,9 @@ if (~((any(bayestopt_.pshape > 0) & options_.mh_replic) | (any(bayestopt_.pshape
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> 0) & options_.load_mh_file)) ...
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> 0) & options_.load_mh_file)) ...
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| ~options_.smoother ) & M_.endo_nbr^2*gend < 1e7 & options_.partial_information == 0 % to be fixed
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| ~options_.smoother ) & M_.endo_nbr^2*gend < 1e7 & options_.partial_information == 0 % to be fixed
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%% ML estimation, or posterior mode without metropolis-hastings or metropolis without bayesian smooth variable
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%% ML estimation, or posterior mode without metropolis-hastings or metropolis without bayesian smooth variable
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[atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,d,decomp] = DsgeSmoother(xparam1,gend,data,data_index,missing_value);
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[atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,decomp] = DsgeSmoother(xparam1,gend,data,data_index,missing_value);
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oo_.Smoother.SteadyState = ys;
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oo_.Smoother.SteadyState = ys;
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oo_.Smoother.TrendCoeffs = trend_coeff;
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oo_.Smoother.TrendCoeffs = trend_coeff;
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oo_.Smoother.integration_order = d;
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oo_.Smoother.variance = P;
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oo_.Smoother.variance = P;
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i_endo = bayestopt_.smoother_saved_var_list;
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i_endo = bayestopt_.smoother_saved_var_list;
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if options_.nk ~= 0
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if options_.nk ~= 0
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