433 lines
16 KiB
Matlab
433 lines
16 KiB
Matlab
function ms_sbvar_setup(options_)
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% function ms_sbvar_setup(options_)
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% does the general file initialization for ms sbvar
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%
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% INPUTS
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% options_: (struct) options
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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 © 2003-2020 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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options_.data = read_variables(options_.datafile, ...
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options_.varobs, [], options_.xls_sheet, options_.xls_range);
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[options_.ms.final_year,options_.ms.final_subperiod] = check_datafile_years_assigned(options_);
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if options_.ms.upper_cholesky
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if options_.ms.lower_cholesky
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error(['Upper Cholesky and lower Cholesky decomposition can''t be ' ...
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'requested at the same time!'])
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else
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options_.ms.restriction_fname = 'upper_cholesky';
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end
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elseif options_.ms.lower_cholesky
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options_.ms.restriction_fname = 'lower_cholesky';
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elseif ~isempty(options_.ms.Qi) && ~isempty(options_.ms.Ri)
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options_.ms.restriction_fname = 'exclusions';
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else
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options_.ms.restriction_fname = 0;
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end
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%==========================================================================
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%== Markov Process Specification File
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%==========================================================================
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markov_file = [options_.ms.output_file_tag '_markov_file.dat'];
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%==========================================================================
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%== BVAR prior
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%==========================================================================
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%=== The following mu is effective only if indxPrior==1.
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%mu = zeros(6,1); % hyperparameters
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if length(options_.ms.coefficients_prior_hyperparameters) ~= 6
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error('When specifying the coefficients_prior_hyperparameters, you must pass a vector of 6 numbers')
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end
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mu = options_.ms.coefficients_prior_hyperparameters;
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mu = reshape(mu,1,6);
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% mu(1): overall tightness for A0 and Aplus
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% mu(2): relative tightness for Aplus
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% mu(3): relative tightness for the constant term
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% mu(4): tightness on lag decay. (1.2 - 1.5 faster decay produces better
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% inflation forecasts
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% mu(5): weight on nvar sums of coeffs dummy observations (unit roots).
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% mu(6): weight on single dummy initial observation including constant
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% (cointegration, unit roots, and stationarity).
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% Alpha on p. 66 for squared time-varying structural shock lambda.
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galp = options_.ms.alpha;
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% Beta on p. 66 for squared time-varying structural shock lambda.
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gbeta = options_.ms.beta;
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% Case 3 (no state change across options_.ms.nlags (l) but allows all variables for a
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% given lag to switch states). Normal prior variance for glamda
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% (nvar-by-nvar for each state) for different variables in lagged D+. See
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% p.71v.
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gsig2_lmdm = options_.ms.gsig2_lmdm;
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%==========================================================================
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%== Data
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%==========================================================================
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% Read in data to produce rectangular array named xdd. Each column is one
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% data series.
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xdd=options_.data;
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% Information about timing of the data for consistancy checks
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% quarters (4) or months (12)
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q_m = options_.ms.freq;
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% beginning year in data set
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yrBin=options_.ms.initial_year;
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% beginning quarter or month in data set
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%options_.ms.initial_subperiod = 1;
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qmBin=options_.ms.initial_subperiod;
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% final year in data set
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yrFin=options_.ms.final_year;
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% final month or quarter in data set
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qmFin=options_.ms.final_subperiod;
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% first year to use in estimation
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yrStart=options_.ms.initial_year;
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% first quarter or month to use in estimation
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qmStart=options_.ms.initial_subperiod;
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% last year to use in estimation
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yrEnd=options_.ms.final_year;
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% last quater or month to use in estimation
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qmEnd=options_.ms.final_subperiod;
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% Log variables in xdd
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logindx = [];
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% Convert percent to decimal in xdd
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pctindx = [];
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% Select the variable to use and rearrange columns if desired
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%vlist = [3 1 2];
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%options_.ms.vlist = [1 2 3];
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options_.ms.vlist = 1:length(options_.varobs);
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vlist1=options_.ms.vlist;
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%==========================================================================
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%==========================================================================
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%==========================================================================
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%== Beginning of code. Modify below at own risk.
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%==========================================================================
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% options that may at some point become user specified
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%indxC0Pres = 0; % 1: cross-A0-and-A+ restrictions; 0: idfile_const is all we have
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indxC0Pres =options_.ms.cross_restrictions;
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% Example for indxOres==1: restrictions of the form P(t) = P(t-1).
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%Rform = 0; % 1: contemporaneous recursive reduced form; 0: restricted (non-recursive) form
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Rform =options_.ms.contemp_reduced_form;
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% % % Pseudo = 0; % 1: Pseudo forecasts; 0: real time forecasts
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%indxPrior = 1; % 1: Bayesian prior; 0: no prior
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indxPrior =options_.ms.bayesian_prior;
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%indxDummy = indxPrior; % 1: add dummy observations to the data; 0: no dummy added.
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indxDummy = options_.ms.bayesian_prior;
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%ndobs = 0; % No dummy observations for xtx, phi, fss, xdatae, etc. Dummy observations are used as an explicit prior in fn_rnrprior_covres_dobs.m.
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%ndobs =options_.ms.dummy_obs;
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%if indxDummy
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% ndobs=nvar+1; % number of dummy observations
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%else
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% ndobs=0; % no dummy observations
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%end
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%
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hpmsmd = [0.0; 0.0];
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indxmsmdeqn = [0; 0; 0; 0]; %This option disenable using this in fn_rnrprior_covres_dobs.m
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nStates = -1;
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%==========================================================================
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%== Create initialization file
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%==========================================================================
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%======================================================================
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%== Check and setup data
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%======================================================================
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% log data
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xdd(:,logindx) = log(xdd(:,logindx));
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% convert percentage to decimal
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xdd(:,pctindx)=.01*xdd(:,pctindx);
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if (q_m ~= 12) && (q_m ~= 4)
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disp('Warning: data must be monthly or quarterly!')
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return
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end
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% number of data points
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nData=(yrFin-yrBin)*q_m + (qmFin-qmBin+1);
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% number of data points in estimation sample
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nSample=(yrEnd-yrStart)*q_m + (qmEnd-qmEnd+1);
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% number of periods not used at beginning of data (non-negative number)
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nStart=(yrStart-yrBin)*q_m + (qmStart-qmBin);
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% number of periods not used at end of data (non-positive number)
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nEnd=(yrEnd-yrFin)*q_m + (qmEnd-qmFin);
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if (nEnd > 0) || (nStart < 0)
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disp('Warning: desired estimation period not in data set!')
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return
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end
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if (nSample <= 0)
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disp('Warning: no data points in estimation period!')
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return
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end
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% reorder variables and create estimation data set
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xdgel=xdd(nStart+1:nData+nEnd,vlist1);
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% bad data points
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baddata = find(isnan(xdgel));
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if ~isempty(baddata)
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disp('Warning: some data for estimation period are unavailable.')
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return
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end
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% set nvar and nexo
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nvar=size(xdgel,2);
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nexo=1;
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% Arranged data information, WITHOUT dummy obs when 0 after mu is used.
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% See fn_rnrprior_covres_dobs.m for using the dummy observations as part of
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% an explicit prior.
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[xtx,xty,yty,fss,phi,y,ncoef,xr,Bh] = fn_dataxy(nvar,options_.ms.nlags,xdgel,mu,0,nexo);
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%======================================================================
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%== Linear Restrictions
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%======================================================================
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if Rform
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Ui=cell(nvar,1); Vi=cell(ncoef,1);
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for kj=1:nvar
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Ui{kj} = eye(nvar); Vi{kj} = eye(ncoef);
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end
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else
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[Ui,Vi,n0,np,ixmC0Pres] = feval(options_.ms.restriction_fname,nvar,nexo,options_.ms);
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if min(n0)==0
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disp('A0: restrictions give no free parameters in one of equations')
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return
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elseif min(np)==0
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disp('Aplus: Restrictions in give no free parameters in one of equations')
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return
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end
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end
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%======================================================================
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%== Estimation
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%======================================================================
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if indxPrior
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%*** Obtains asymmetric prior (with no linear restrictions) with dummy observations as part of an explicit prior (i.e,
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% reflected in Hpmulti and Hpinvmulti). See Forecast II, pp.69a-69b for details.
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if 1 % Liquidity effect prior on both MS and MD equations.
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[Pi,H0multi,Hpmulti,H0invmulti,Hpinvmulti] = fn_rnrprior_covres_dobs(nvar,q_m,options_.ms.nlags,xdgel,mu,indxDummy,hpmsmd,indxmsmdeqn);
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else
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[Pi,H0multi,Hpmulti,H0invmulti,Hpinvmulti] = fn_rnrprior(nvar,q_m,options_.ms.nlags,xdgel,mu);
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end
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%*** Combines asymmetric prior with linear restrictions
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[Ptld,H0invtld,Hpinvtld] = fn_rlrprior(Ui,Vi,Pi,H0multi,Hpmulti,nvar);
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%*** Obtains the posterior matrices for estimation and inference
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[Pmat,H0inv,Hpinv] = fn_rlrpostr(xtx,xty,yty,Ptld,H0invtld,Hpinvtld,Ui,Vi);
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else
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%*** Obtain the posterior matrices for estimation and inference
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[Pmat,H0inv,Hpinv] = fn_dlrpostr(xtx,xty,yty,Ui,Vi);
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end
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if Rform
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%*** Obtain the ML estimate
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A0hatinv = chol(H0inv{1}/fss); % upper triangular but lower triangular choleski
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A0hat=inv(A0hatinv);
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Aphat = Pmat{1}*A0hat;
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else
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%*** Obtain the ML estimate
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% load idenml
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x = 10*rand(sum(n0),1);
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H0 = eye(sum(n0));
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crit = 1.0e-9;
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nit = 10000;
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%
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[fhat,xhat,grad,Hhat,itct,fcount,retcodehat] = csminwel('fn_a0freefun',x,H0,'fn_a0freegrad',crit,nit,Ui,nvar,n0,fss,H0inv);
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A0hat = fn_tran_b2a(xhat,Ui,nvar,n0);
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xhat = fn_tran_a2b(A0hat,Ui,nvar,n0);
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[Aphat,ghat] = fn_gfmean(xhat,Pmat,Vi,nvar,ncoef,n0,np);
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if indxC0Pres
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Fhatur0P = Fhat; % ur: unrestriced across A0 and A+
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for ki = 1:size(ixmC0Pres,1) % loop through the number of equations in which
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% cross-A0-A+ restrictions occur. See St. Louis Note p.5.
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ixeq = ixmC0Pres{ki}(1,1); % index for the jth equation in consideration.
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Lit = Vi{ixeq}(ixmC0Pres{ki}(:,2),:); % transposed restriction matrix Li
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% V_j(i,:) in f_j(i) = V_j(i,:)*g_j
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ci = ixmC0Pres{ki}(:,4) .* A0hat(ixmC0Pres{ki}(:,3),ixeq);
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% s * a_j(h) in the restriction f_j(i) = s * a_j(h).
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LtH = Lit/Hpinv{ixeq};
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HLV = LtH'/(LtH*Lit');
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gihat = Vi{ixeq}'*Fhatur0P(:,ixeq);
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Aphat(:,ixeq) = Vi{ixeq}*(gihat + HLV*(ci-Lit*gihat));
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end
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end
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end
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%======================================================================
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%== Create matlab initialization file
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%======================================================================
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matlab_filename = ['matlab_',options_.ms.output_file_tag,'.prn'];
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fidForC = fopen(matlab_filename,'w');
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fprintf(fidForC,'\n%s\n','//== gxia: alpha parameter for gamma prior of xi ==//');
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fprintf(fidForC,' %20.15f ', galp);
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fprintf(fidForC, '\n\n');
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fprintf(fidForC,'\n%s\n','//== gxib: beta parameter for gamma prior of xi ==//');
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fprintf(fidForC,' %20.15f ', gbeta);
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fprintf(fidForC, '\n\n');
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fprintf(fidForC,'\n%s\n','//== glamdasig: sigma parameter for normal prior of lamda ==//');
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fprintf(fidForC,' %20.15f ', sqrt(gsig2_lmdm));
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fprintf(fidForC, '\n\n');
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%=== lags, nvar, nStates, sample size (excluding options_.ms.nlags where, with dummyies, fss=nSample-options_.ms.nlags+ndobs).
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fprintf(fidForC,'\n%s\n','//== lags, nvar, nStates, T ==//');
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fprintf(fidForC,' %d %d %d %d\n\n\n',options_.ms.nlags, nvar, nStates, fss);
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%=== A0hat nvar-by-nvar from the constant VAR.
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fprintf(fidForC,'\n%s\n','//== A0hat: nvar-by-nvar ==//');
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indxFloat = 1;
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xM = A0hat;
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nrows = nvar;
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ncols = nvar;
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fn_fprintmatrix(fidForC, xM, nrows, ncols, indxFloat)
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%=== Aphat ncoef-by-nvar from the constant VAR.
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%=== Each column of Aphat is in the order of [nvar variables for 1st lag, ..., nvar variables for last lag, constant term].
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fprintf(fidForC,'\n%s\n','//== Aphat: ncoef(lags*nvar+1)-by-nvar ==//');
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indxFloat = 1;
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xM = Aphat;
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nrows = ncoef;
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ncols = nvar;
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fn_fprintmatrix(fidForC, xM, nrows, ncols, indxFloat)
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%=== n0const: nvar-by-1, whose ith element represents the number of free A0 parameters in ith equation for the case of constant parameters.
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fprintf(fidForC,'\n%s\n','//== n0const: nvar-by-1 ==//');
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indxFloat = 0;
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xM = n0;
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nrows = 1;
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ncols = nvar;
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fn_fprintmatrix(fidForC, xM', nrows, ncols, indxFloat)
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%=== npconst: nvar-by-1, whose ith element represents the number of free A+ parameters in ith equation for the case of constant parameters.
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fprintf(fidForC,'\n%s\n','//== npconst: nvar-by-1 ==//');
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indxFloat = 0;
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xM = np;
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nrows = 1;
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ncols = nvar;
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fn_fprintmatrix(fidForC, xM', nrows, ncols, indxFloat)
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%=== Specification
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fprintf(fidForC,'\n%s','//== Specification (0=default 1=Sims-Zha 2=Random Walk) ==//');
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fprintf(fidForC,'\n%d\n\n',options_.ms.specification);
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%=== Uiconst: nvar-by-1 cell. In each cell, nvar-by-qi orthonormal basis for the null of the ith
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% equation contemporaneous restriction matrix where qi is the number of free parameters.
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% With this transformation, we have ai = Ui*bi or Ui'*ai = bi where ai is a vector
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% of total original parameters and bi is a vector of free parameters. When no
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% restrictions are imposed, we have Ui = I. There must be at least one free
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% parameter left for the ith equation.
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fprintf(fidForC,'\n%s\n','//== Uiconst: cell(nvar,1) and nvar-by-n0const(i) for the ith cell (equation) ==//');
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for i_=1:nvar
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fn_fprintmatrix(fidForC, Ui{i_}, nvar, n0(i_), 1);
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end
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%=== Viconst: nvar-by-1 cell. In each cell, k-by-ri orthonormal basis for the null of the ith
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% equation lagged restriction matrix where k is a total of exogenous variables and
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% ri is the number of free parameters. With this transformation, we have fi = Vi*gi
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% or Vi'*fi = gi where fi is a vector of total original parameters and gi is a
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% vector of free parameters. There must be at least one free parameter left for
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% the ith equation.
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fprintf(fidForC,'\n%s\n','//== Viconst: cell(nvar,1) and ncoef-by-n0const(i) for the ith cell (equation) ==//');
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for i_=1:nvar
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fn_fprintmatrix(fidForC, Vi{i_}, ncoef, np(i_), 1);
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end
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%=== H0barconstcell: cell(nvar,1) (equations) and n-by-n for each cell (equaiton).
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%=== H0barconst: prior covariance matrix for each column of A0 under asymmetric prior (including SZ dummy obs.) with NO linear restrictions imposed yet.
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fprintf(fidForC,'\n%s\n','//== H0barconstcell: cell(nvar,1) and n-by-n for the ith cell (equation) ==//');
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for i_=1:nvar
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fn_fprintmatrix(fidForC, H0multi(:,:,i_), nvar, nvar, 1);
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end
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%=== Hpbarconstcell: cell(nvar,1) (equations) and ncoef-by-ncoef for each cell (equaiton).
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%=== Hpbarconst: prior covariance matrix for each column of A+ under asymmetric prior (including SZ dummy obs.) with NO linear restrictions imposed yet.
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fprintf(fidForC,'\n%s\n','//== Hpbarconstcell: cell(nvar,1) and ncoef-by-ncoef for the ith cell (equation) ==//');
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for i_=1:nvar
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fn_fprintmatrix(fidForC, Hpmulti(:,:,i_), ncoef, ncoef, 1);
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end
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%=== phi: X; T-by-k; column: [nvar for 1st lag, ..., nvar for last lag, other exogenous terms, const term]
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fprintf(fidForC,'\n%s\n','//== Xright -- X: T-by-ncoef ==//');
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xM = phi;
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nrows = fss;
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ncols = ncoef;
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for ki=1:nrows
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for kj=1:ncols
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fprintf(fidForC,' %20.15f ',xM((kj-1)*nrows+ki));
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if (kj==ncols)
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fprintf(fidForC,'\n');
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end
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end
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if (ki==nrows)
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fprintf(fidForC,'\n\n');
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end
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end
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%=== y: Y: T-by-nvar where T=fss
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fprintf(fidForC,'\n%s\n','//== Yleft -- Y: T-by-nvar ==//');
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xM = y;
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nrows = fss;
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ncols = nvar;
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for ki=1:nrows
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for kj=1:ncols
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fprintf(fidForC,' %20.15f ',xM((kj-1)*nrows+ki));
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if (kj==ncols)
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fprintf(fidForC,'\n');
|
|
end
|
|
end
|
|
if (ki==nrows)
|
|
fprintf(fidForC,'\n\n');
|
|
end
|
|
end
|
|
|
|
fclose(fidForC);
|
|
|
|
%======================================================================
|
|
%== Create C initialization filename
|
|
%======================================================================
|
|
ms_write_markov_file(markov_file,options_)
|
|
create_init_file = [matlab_filename,' ',markov_file,' ',options_.ms.file_tag];
|
|
ms_sbvar_create_init_file(create_init_file);
|
|
end
|