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corrected bug linked to new handling of datasets

time-shift
Michel Juillard 2011-11-01 16:44:31 +01:00
parent d14d0f8b45
commit 02b86795b2
1 changed files with 17 additions and 34 deletions
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51
matlab/imcforecast.m
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@ -99,43 +99,26 @@ if estimated_model
set_parameters(xparam);
n_varobs = size(options_.varobs,1);
rawdata = read_variables(options_.datafile,options_.varobs,[],options_.xls_sheet,options_.xls_range);
options_ = set_default_option(options_,'nobs',size(rawdata,1)-options_.first_obs+1);
gend = options_.nobs;
rawdata = rawdata(options_.first_obs:options_.first_obs+gend-1,:);
% Transform the data.
if options_.loglinear
if ~options_.logdata
rawdata = log(rawdata);
end
% Load and transform data.
transformation = [];
if options_.loglinear && ~options_.logdata
transformation = @log;
end
% Test if the data set is real.
if ~isreal(rawdata)
error('There are complex values in the data! Probably a wrong transformation')
xls.sheet = options_.xls_sheet;
xls.range = options_.xls_range;
if ~isfield(options_,'nobs')
options_.nobs = [];
end
% Detrend the data.
options_.missing_data = any(any(isnan(rawdata)));
if options_.prefilter == 1
if options_.missing_data
bayestopt_.mean_varobs = zeros(n_varobs,1);
for variable=1:n_varobs
rdx = find(~isnan(rawdata(:,variable)));
m = mean(rawdata(rdx,variable));
rawdata(rdx,variable) = rawdata(rdx,variable)-m;
bayestopt_.mean_varobs(variable) = m;
end
else
bayestopt_.mean_varobs = mean(rawdata,1)';
rawdata = rawdata-repmat(bayestopt_.mean_varobs',gend,1);
end
end
data = transpose(rawdata);
% Handle the missing observations.
[data_index,number_of_observations,no_more_missing_observations] = describe_missing_data(data);
missing_value = ~(number_of_observations == gend*n_varobs);
dataset_ = initialize_dataset(options_.datafile,options_.varobs,options_.first_obs,options_.nobs,transformation,options_.prefilter,xls);
[atT,innov,measurement_error,filtered_state_vector,ys,trend_coeff] = DsgeSmoother(xparam,gend,data,data_index,number_of_observations);
data = dataset_.data;
data_index = dataset_.missing.aindex;
gend = options_.nobs;
missing_value = dataset_.missing.state;
[atT,innov,measurement_error,filtered_state_vector,ys,trend_coeff] = DsgeSmoother(xparam,gend,data,data_index,missing_value);
trend = repmat(ys,1,options_cond_fcst.periods+1);
for i=1:M_.endo_nbr