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Fixed bugs: 

(1) The non_linear_dsge_likelihood routine was not adapted to the last changes related to the way data are handled.

(2) The removal of the trend was missing.
time-shift
Stéphane Adjemian (Scylla) 2014-08-28 16:59:52 +02:00
parent 06f4926553
commit 77a29e95c1
1 changed files with 6 additions and 9 deletions
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15
matlab/non_linear_dsge_likelihood.m
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@ -1,4 +1,4 @@
function [fval,ys,trend_coeff,exit_flag,info,Model,DynareOptions,BayesInfo,DynareResults] = non_linear_dsge_likelihood(xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
function [fval,ys,trend_coeff,exit_flag,info,Model,DynareOptions,BayesInfo,DynareResults] = non_linear_dsge_likelihood(xparam1,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
% Evaluates the posterior kernel of a dsge model using a non linear filter.
%@info:
@ -138,9 +138,6 @@ if DynareOptions.loglinear
error('non_linear_dsge_likelihood: It is not possible to use a non linear filter with the option loglinear!')
end
% Set the number of observed variables
nvobs = DynareDataset.info.nvobs;
%------------------------------------------------------------------------------
% 1. Get the structural parameters & define penalties
%------------------------------------------------------------------------------
@ -232,30 +229,30 @@ BayesInfo.mf = BayesInfo.mf1;
% Define the deterministic linear trend of the measurement equation.
if DynareOptions.noconstant
constant = zeros(nvobs,1);
constant = zeros(DynareDataset.vobs,1);
else
constant = SteadyState(BayesInfo.mfys);
end
% Define the deterministic linear trend of the measurement equation.
if BayesInfo.with_trend
trend_coeff = zeros(DynareDataset.info.nvobs,1);
trend_coeff = zeros(DynareDataset.vobs,1);
t = DynareOptions.trend_coeffs;
for i=1:length(t)
if ~isempty(t{i})
trend_coeff(i) = evalin('base',t{i});
end
end
trend = repmat(constant,1,DynareDataset.info.ntobs)+trend_coeff*[1:DynareDataset.info.ntobs];
trend = repmat(constant,1,DynareDataset.nobs)+trend_coeff*[1:DynareDataset.nobs];
else
trend = repmat(constant,1,DynareDataset.info.ntobs);
trend = repmat(constant,1,DynareDataset.nobs);
end
% Get needed informations for kalman filter routines.
start = DynareOptions.presample+1;
np = size(T,1);
mf = BayesInfo.mf;
Y = transpose(DynareDataset.rawdata);
Y = transpose(DynareDataset.data)-trend;
%------------------------------------------------------------------------------
% 3. Initial condition of the Kalman filter