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redo code for recursive estimation 

corrected bugs with option prefilter, bayestopt_.mean_varobs is now a column vector

git-svn-id: https://www.dynare.org/svn/dynare/trunk@2369 ac1d8469-bf42-47a9-8791-bf33cf982152
time-shift
michel 2009-01-22 21:34:15 +00:00
parent e98844dc61
commit d5a1a025d9
5 changed files with 1672 additions and 1496 deletions
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1659
matlab/dynare_estimation.m
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1483
matlab/dynare_estimation_1.m Normal file
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4
matlab/dynare_estimation_init.m
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@ -238,8 +238,8 @@ if options_.loglinear == 1 & ~options_.logdata
rawdata = log(rawdata);
end
if options_.prefilter == 1
bayestopt_.mean_varobs = mean(rawdata,1);
data = transpose(rawdata-ones(gend,1)*bayestopt_.mean_varobs);
bayestopt_.mean_varobs = mean(rawdata,1)';
data = transpose(rawdata-repmat(bayestopt_.mean_varobs',gend,1));
else
data = transpose(rawdata);
end

18
matlab/forecast.m
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@ -41,12 +41,8 @@ function info = forecast(var_list,task)
old_options = options_;
maximum_lag = M_.maximum_lag;
options_ = set_default_option(options_,'periods',40);
if options_.periods == 0
options_.periods = 40;
end
horizon = options_.periods;
options_ = set_default_option(options_,'conf_sig',0.9);
horizon = options_.forecast;
endo_names = M_.endo_names;
if isempty(var_list)
@ -100,17 +96,17 @@ function info = forecast(var_list,task)
end
if M_.exo_det_nbr == 0
[yf,int_width] = forcst(oo_.dr,y0,options_.periods,var_list);
[yf,int_width] = forcst(oo_.dr,y0,horizon,var_list);
else
exo_det_length = size(oo_.exo_det_simul,1);
if options_.periods > exo_det_length
ex = zeros(options_.periods,M_.exo_nbr);
if horizon > exo_det_length
ex = zeros(horizon,M_.exo_nbr);
oo_.exo_det_simul = [ oo_.exo_det_simul;...
repmat(oo_.exo_det_steady_state',...
options_.periods- ...
horizon- ...
exo_det_length,1)];
%ex_det_length,1),1)];
elseif options_.periods < exo_det_length
elseif horizon < exo_det_length
ex = zeros(exo_det_length,M_.exo_nbr);
end
[yf,int_width] = simultxdet(y0,ex,oo_.exo_det_simul,...

4
matlab/imcforecast.m
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@ -29,8 +29,8 @@ if options_.loglinear == 1 & ~options_.logdata
rawdata = log(rawdata);
end
if options_.prefilter == 1
bayestopt_.mean_varobs = mean(rawdata,1);
data = transpose(rawdata-ones(gend,1)*bayestopt_.mean_varobs);
bayestopt_.mean_varobs = mean(rawdata,1)';
data = transpose(rawdata-repmat(bayestopt_.mean_varobs',gend,1));
else
data = transpose(rawdata);
end