Correct typos in error message and header
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@ -1,15 +1,16 @@
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function [dr,info,M_,options_,oo_] = dr_block(dr,task,M_,options_,oo_,varargin)
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% function [dr,info,M_,options_,oo_] = dr_block(dr,task,M_,options_,oo_)
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% computes the reduced form solution of a rational expectation model (first
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% approximation of the stochastic model around the deterministic steady state).
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% function [dr,info,M_,options_,oo_] = dr_block(dr,task,M_,options_,oo_,varargin)
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% computes the reduced form solution of a rational expectations model
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% (first order approximation of the stochastic model around the deterministic steady state).
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%
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% INPUTS
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% dr [matlab structure] Decision rules for stochastic simulations.
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% task [integer] if task = 0 then dr1 computes decision rules.
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% if task = 1 then dr1 computes eigenvalues.
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% task [integer] if task = 0 then dr_block computes decision rules.
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% if task = 1 then dr_block computes eigenvalues.
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% M_ [matlab structure] Definition of the model.
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% options_ [matlab structure] Global options.
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% oo_ [matlab structure] Results
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% oo_ [matlab cell] Other input arguments
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%
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% OUTPUTS
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% dr [matlab structure] Decision rules for stochastic simulations.
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@ -33,7 +34,7 @@ function [dr,info,M_,options_,oo_] = dr_block(dr,task,M_,options_,oo_,varargin)
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% none.
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%
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% Copyright (C) 2010-2015 Dynare Team
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% Copyright (C) 2010-2016 Dynare Team
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%
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% This file is part of Dynare.
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%
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@ -638,7 +639,7 @@ for i = 1:Size;
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if options_.loglinear
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error('log linear option is for the moment not supported in first order approximation for a block decomposed mode');
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error('The loglinear option is not yet supported in first order approximation for a block decomposed model');
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% k = find(dr.kstate(:,2) <= M_.maximum_endo_lag+1);
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% klag = dr.kstate(k,[1 2]);
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% k1 = dr.order_var;
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@ -657,7 +658,7 @@ for i = 1:Size;
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%exogenous deterministic variables
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if exo_det_nbr > 0
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error('deterministic exogenous are not yet implemented in first order approximation for a block decomposed model');
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error('Deterministic exogenous variables are not yet implemented in first order approximation for a block decomposed model');
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% f1 = sparse(jacobia_(:,nonzeros(M_.lead_lag_incidence(M_.maximum_endo_lag+2:end,order_var))));
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% f0 = sparse(jacobia_(:,nonzeros(M_.lead_lag_incidence(M_.maximum_endo_lag+1,order_var))));
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% fudet = data(i).g1_xd;
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@ -1,5 +1,5 @@
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function dyn_latex_table(M_,options_,title,LaTeXtitle,headers,labels,values,label_width,val_width,val_precis,optional_header)
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%function dyn_latex_table(M_,title,LaTeXtitle,headers,labels,values,label_width,val_width,val_precis,optional_header)
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%function dyn_latex_table(M_,options_,title,LaTeXtitle,headers,labels,values,label_width,val_width,val_precis,optional_header)
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% Copyright (C) 2015-2016 Dynare Team
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%
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