Added a matlab function to generate artificial datasets with missing observations.
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function data = simulate_data_with_missing_observations(n,m,S,options)
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% Simulates data with missing observations.
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%
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% We simulate data using a n-dimensional VAR(1) model.
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%
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% INPUTS
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% n [integer] scalar, number of variables.
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% m [integer] scalar, number of observed variables.
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% S [integer] scalar, maximum number of observations per observed variable.
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% options [struct] structure of options:
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% * if options.missing_info{1} = 1 the missing variables are at the beginning of the sample.
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% * if options.missing_info{1} = 2 the missing observations are at the end of the sample.
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% * if options.missing_info{1} = 3 the missing observations are randomly distributed.
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% * options.missing_info{2} is a vector of integer designing the observed variables for which observations are missing.
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% * if options.missing_info{3} is an integer scalar then it defines the number of missing observations per variable.
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% * if options.missing_info{3} is a double scalar (in [0,1]) it defines the frequency of missing observations per variable.
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% * options.unit_root_info is a scalar integer specifying the number of unit roots in the model.
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%
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% OUTPUTS
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% none
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%
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% SPECIAL REQUIREMENTS
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% none
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% Copyright (C) 2010 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 miy
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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 <http://www.gnu.org/licenses/>.
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if n<=m
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error('n must be greater than m!')
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end
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% Build the autoregressive matrix.
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T_eigenvalues = rand(n,1)*2-1;
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if options.unit_root_info
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T_eigenvalues(1:options.unit_root_info) = ones(options.unit_root_info,1);
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end
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T_eigenvectors = randn(n,n);
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T = T_eigenvectors*diag(T_eigenvalues)*inv(T_eigenvectors);
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% Simulate the VAR(1) model.
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data = zeros(n,S+100);
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for t=2:size(data,2)
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data(:,t) = T*data(:,t-1)+.01*randn(n,1);
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end
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% Select the observed variables.
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data = transpose(data(1:m,100+1:end));
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% Remove observations.
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if options.missing_info{1}==1
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data(1:options.missing_info{3},options.missing_info{2}) = NaN ;
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elseif options.missing_info{1}==2
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data(S:S-options.missing_info{3},options.missing_info{2}) = NaN ;
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elseif options.missing_info{1}==3
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for i=1:length(options.missing_info{2})
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idx = randi(T,ceil(options.missing_info{3}),1);
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data(idx,i) = NaN;
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end
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else
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error('Unknown option!')
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end
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