103 lines
3.6 KiB
Matlab
103 lines
3.6 KiB
Matlab
function dataset_ = initialize_dataset(datafile,varobs,first,nobs,logged_data_flag,prefilter,xls)
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% Initializes a structure describing the dataset.
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% Copyright © 2011-2017 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
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if isempty(datafile)
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error('Estimation::initialize_dataset: You have to declare a dataset file!')
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end
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if isempty(varobs)
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error('Estimation::initialize_dataset: You have to declare a set of observed variables')
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end
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% Get raw data.
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rawdata = read_variables(datafile,varobs,[],xls.sheet,xls.range);
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% Get the (default) number of observations.
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if isempty(nobs) || rows(rawdata)<nobs+first-1 %case 2: dataset has changed
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nobs = rows(rawdata)-first+1;
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end
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% Set the (default) prefilter option.
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if isempty(prefilter)
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prefilter = 0;
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end
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% Fill the dataset structure
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dataset_.info.ntobs = nobs;
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dataset_.info.nvobs = length(varobs);
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dataset_.info.varobs = varobs;
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% Test the number of variables in the database.
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if dataset_.info.nvobs-size(rawdata,2)
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skipline()
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disp(['Declared number of observed variables = ' int2str(dataset.info.nvobs)])
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disp(['Number of variables in the database = ' int2str(size(rawdata,2))])
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skipline()
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error(['Estimation can''t take place because the declared number of observed' ...
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'variables doesn''t match the number of variables in the database.'])
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end
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if size(rawdata,1)~=dataset_.info.ntobs
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fprintf('Restricting the sample to observations %d to %d. Using in total %d observations. \n',first,first+dataset_.info.ntobs-1,dataset_.info.ntobs)
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end
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rawdata = rawdata(first:(first+dataset_.info.ntobs-1),:);
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% Take the log of the variables if needed
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if logged_data_flag
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dataset_.rawdata = log(rawdata);
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else
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if isequal(transformation,@log)
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if ~isreal(rawdata)
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error(['Estimation::initialize_dataset: Some of the variables have non positive observations, I cannot take the log of the data!'])
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end
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end
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dataset_.rawdata = arrayfun(transformation,rawdata);
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end
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% Test if the observations are real numbers.
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if ~isreal(dataset_.rawdata)
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error('Estimation::initialize_dataset: There are complex values in the data!')
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end
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% Test for missing observations.
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dataset_.missing.state = any(any(isnan(dataset_.rawdata)));
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if dataset_.missing.state
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[i,n,s,j] = describe_missing_data(dataset_.rawdata);
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dataset_.missing.aindex = i;
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dataset_.missing.vindex = j;
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dataset_.missing.number_of_observations = n;
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dataset_.missing.no_more_missing_observations = s;
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else
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dataset_.missing.aindex = num2cell(repmat(1:dataset_.info.nvobs,dataset_.info.ntobs,1)',1);
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dataset_.missing.vindex = [];
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dataset_.missing.number_of_observations = [];
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dataset_.missing.no_more_missing_observations = 1;
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end
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% Compute the empirical mean of the observed variables..
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dataset_.descriptive.mean = nanmean(dataset_.rawdata);
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% Prefilter the data if needed.
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if prefilter == 1
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dataset_.data = transpose(bsxfun(@minus,dataset_.rawdata,dataset_.descriptive.mean));
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else
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dataset_.data = transpose(dataset_.rawdata);
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end
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