v4: removed obsolete functions olr.m olr1.m olr2.m
git-svn-id: https://www.dynare.org/svn/dynare/dynare_v4@851 ac1d8469-bf42-47a9-8791-bf33cf982152time-shift
michel
parent
60e9bf9497
commit
4a6e4ebe13
133
matlab/olr.m
133
matlab/olr.m
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@ -1,133 +0,0 @@
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% Copyright (C) 2001 Michel Juillard
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%
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function olr(var_list,olr_inst,obj_var,W)
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global M_ options_ oo_
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options_.order = 1;
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options_ = set_default_option(options_,'ar',5);
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options_ = set_default_option(options_,'irf',40);
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options_ = set_default_option(options_,'dr_algo',0);
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options_ = set_default_option(options_,'simul_algo',0);
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options_ = set_default_option(options_,'drop',100);
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options_ = set_default_option(options_,'replic',1);
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options_ = set_default_option(options_,'nomoments',0);
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options_ = set_default_option(options_,'nocorr',0);
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options_ = set_default_option(options_,'simul_seed',[]);
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options_ = set_default_option(options_,'hp_filter',0);
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options_ = set_default_option(options_,'hp_ngrid',512);
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options_ = set_default_option(options_,'simul',0);
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options_ = set_default_option(options_,'olr_beta',1);
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options_ = set_default_option(options_,'periods',1);
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if options_.simul & ~isempty(options_.periods) & options_.periods == 0
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options_.periods = options_.periods;
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end
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options_.periods = max(options_.periods,1);
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make_ex_;
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oo_.dr=olr1(oo_.steady_state,options_.dr_algo,olr_inst,options_.olr_beta,obj_var,W);
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disp(' ')
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disp('OPTIMAL LINEAR REGULATOR')
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disp(' ')
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disp('MODEL SUMMARY')
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disp(' ')
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disp([' Number of variables: ' int2str(M_.endo_nbr-size(olr_inst,1))])
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disp([' Number of stochastic shocks: ' int2str(M_.exo_nbr)])
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disp([' Number of instruments ' int2str(size(olr_inst,1))])
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% disp([' Number of state variables: ' ...
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% int2str(length(find(oo_.dr.kstate(:,2) <= M_.maximum_lag+1)))])
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% disp([' Number of jumpers: ' ...
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% int2str(length(find(oo_.dr.kstate(:,2) == M_.maximum_lag+2)))])
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disp([' Number of static variables: ' int2str(oo_.dr.nstatic)])
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my_title='MATRIX OF COVARIANCE OF EXOGENOUS SHOCKS';
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labels = deblank(M_.exo_name);
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headers = strvcat('Variables',labels);
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lh = size(labels,2)+2;
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table(my_title,headers,labels,M_.Sigma_e,lh,10,6);
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disp(' ')
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disp_dr(oo_.dr,options_.order,var_list);
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if options_.order == 1 & options_.simul == 0 & options_.nomoments == 0
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disp_th_moments(oo_.dr,var_list);
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elseif options_.simul == 1 | options_.nomoments == 0
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if options_.periods == 0
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error('OLR error: number of periods for the simulation isn''t specified')
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end
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if options_.periods < options_.drop
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disp(['OLR error: The horizon of simulation is shorter' ...
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' than the number of observations to be DROPed'])
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return
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end
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oo_.y_simul = simult(repmat(oo_.dr.ys,1,M_.maximum_lag),oo_.dr);
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dyn2vec;
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if options_.nomoments == 0
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disp_moments(oo_.y_simul,var_list);
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end
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end
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n = size(var_list,1);
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if n == 0
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n = length(oo_.dr.order_var);
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ivar = [1:n]';
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var_list = M_.endo_names;
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else
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ivar=zeros(n,1);
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for i=1:n
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i_tmp = strmatch(var_list(i,:),M_.endo_names,'exact');
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if isempty(i_tmp)
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error (['One of the specified variables does not exist']) ;
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else
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ivar(i) = i_tmp;
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end
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end
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end
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if n < 13 & options_.irf > 0
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if n == 1
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nr = 1;
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nc = 1;
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elseif n == 2
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nr = 1;
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nc = 2;
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elseif n <= 4
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nr = 2;
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nc = 2;
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elseif n <= 6
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nr = 2;
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nc = 3;
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elseif n <= 9
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nr = 3;
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nc = 3;
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elseif n <= 12
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nr = 3;
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nc = 4;
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end
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olditer = options_.periods;
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if options_.order == 1
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options_.replic = 1;
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else
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if options_.replic == 0
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options_.replic = 50;
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end
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end
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for i = 1:M_.exo_nbr
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figure('Name',['Shock to ' M_.exo_name(i,:)]);
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y=irf(oo_.dr,M_.exo_name(i,:),sqrt(M_.Sigma_e(i,i)), options_.irf, options_.drop, options_.replic, options_.order);
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m = 1;
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for j = 1:n
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if max(y(ivar(j),:))-min(y(ivar(j),:)) > 1e-10
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subplot(nr,nc,m);
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plot([y(ivar(j),:)']);
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title(var_list(j,:),'Interpreter','none');
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assignin('base',[deblank(var_list(j,:)) '_' deblank(M_.exo_name(i,:))],y(ivar(j),:)');
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m = m + 1;
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end
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end
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end
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options_.periods = olditer;
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end
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% 05/21/03 MJ
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@ -1,52 +0,0 @@
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% Copyright (C) 2001 Michel Juillard
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%
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function dr = olr1(ys,algo,olr_inst,bet,obj_var,W)
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global M_ options_ oo_
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global it_ means_ stderrs_
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xlen = M_.maximum_lead + M_.maximum_lag + 1;
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klen = M_.maximum_lag + M_.maximum_lead + 1;
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iyv = M_.lead_lag_incidence';
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iyv = iyv(:);
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iyr0 = find(iyv) ;
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it_ = M_.maximum_lag + 1 ;
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if M_.exo_nbr == 0
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oo_.exo_steady_state = [] ;
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end
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if ~ M_.lead_lag_incidence(M_.maximum_lag+1,:) > 0
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error ('OLR: Error in model specification: some variables don"t appear as current') ;
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end
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if M_.maximum_lead == 0
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error ('Backward or static model: no point in using OLR') ;
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end
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if xlen > 1
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error (['OLR: stochastic exogenous variables must appear only at the' ...
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' current period. Use additional endogenous variables']) ;
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end
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% check if ys is steady state
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tempex = oo_.exo_simul;
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oo_.exo_simul = oo_.exo_steady_state';
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fh = str2func([M_.fname '_static']);
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if max(abs(feval(fh,ys))) > options_.dynatol
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[dr.ys, check] = dynare_solve([M_.fname '_static'],ys);
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if check
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error('OLR: convergence problem in DYNARE_SOLVE')
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end
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else
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dr.ys = ys;
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end
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dr = olr2(dr,olr_inst,bet,obj_var,W);
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oo_.exo_simul = tempex;
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tempex = [];
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% 04/13/03 MJ
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334
matlab/olr2.m
334
matlab/olr2.m
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@ -1,334 +0,0 @@
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% Copyright (C) 2003 Michel Juillard
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%
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% computes an optimal policy as the optimal linear regulator
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%
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function dr=olr2(dr,olr_inst,bet,obj_var,W)
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global M_ options_ oo_
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global it_ stdexo_
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xlen = M_.maximum_lead + M_.maximum_lag + 1;
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klen = M_.maximum_lag + M_.maximum_lead + 1;
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iyv = M_.lead_lag_incidence';
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iyv = iyv(:);
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iyr0 = find(iyv) ;
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it_ = M_.maximum_lag + 1 ;
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inst_nbr = size(olr_inst,1);
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inst_i = zeros(inst_nbr,1);
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for i=1:inst_nbr
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k = strmatch(olr_inst(i,:),M_.endo_names,'exact');
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if isempty(k)
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error(sprintf('OLR_INST %s isn''t a declared variable'));
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else
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inst_i(i) = k;
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end
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end
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if M_.maximum_lead == 0
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error ('OLR : No forward variable: no point in using OLR') ;
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end
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if find(any(M_.lead_lag_incidence([1:M_.maximum_lag M_.maximum_lag+2:M_.maximum_lead],inst_i),2))
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error('OLR: instruments can only appear at the current period');
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end
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non_inst_i = setdiff([1:M_.endo_nbr],inst_i);
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iy1_ = M_.lead_lag_incidence(:,non_inst_i);
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endo_nbr_1 = M_.endo_nbr - inst_nbr;
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if M_.exo_nbr == 0
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oo_.exo_steady_state = [] ;
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end
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if ~ M_.lead_lag_incidence(M_.maximum_lag+1,:) > 0
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error ('Error in model specification: some variables don''t appear as current') ;
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end
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if xlen > 1
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error (['SS: stochastic exogenous variables must appear only at the' ...
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' current period. Use additional endogenous variables']) ;
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end
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tempex = oo_.exo_simul;
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it_ = M_.maximum_lag + 1;
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dr.ys = oo_.steady_state;
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z = repmat(dr.ys,1,klen);
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z = z(iyr0) ;
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%M_.jacobia=real(jacob_a('ff1_',[z; oo_.exo_steady_state])) ;
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[junk,M_.jacobia] = feval([M_.fname '_dynamic'],z,oo_.exo_simul);
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oo_.exo_simul = tempex ;
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tempex = [];
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nz = size(z,1);
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fwrd_var = find(any(M_.lead_lag_incidence(M_.maximum_lag+2:end,:),1))';
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if M_.maximum_lag > 0
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pred_var = find(any(M_.lead_lag_incidence(1:M_.maximum_lag,:),1))';
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both_var = intersect(pred_var,fwrd_var);
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pred_var = setdiff(pred_var,both_var);
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fwrd_var = setdiff(fwrd_var,both_var);
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stat_var = setdiff([1:M_.endo_nbr]',union(union(pred_var,both_var),fwrd_var)); % static variables
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else
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pred_var = [];
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both_var = [];
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stat_var = setdiff([1:M_.endo_nbr]',fwrd_var);
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end
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stat_var = setdiff(stat_var,inst_i);
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% static variables in objective function
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[stat_obj_var] = intersect(obj_var,stat_var);
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n_stat_obj_var = length(stat_obj_var);
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pred_var = [stat_obj_var; pred_var];
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nboth = length(both_var);
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npred = length(pred_var);
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nfwrd = length(fwrd_var);
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nstatic1 = length(stat_var);
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nstatic = nstatic1-n_stat_obj_var;
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order_var = [ setdiff(stat_var,obj_var); pred_var; both_var; fwrd_var];
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k1 = M_.lead_lag_incidence(find([1:klen] ~= M_.maximum_lag+1),:);
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b = M_.jacobia(1:endo_nbr_1,M_.lead_lag_incidence(M_.maximum_lag+1,order_var));
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a = b\M_.jacobia(1:endo_nbr_1,nonzeros(k1'));
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if M_.exo_nbr
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fu = -b\M_.jacobia(1:endo_nbr_1,nz+1:end);
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end
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% instruments' effects
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b = -b\M_.jacobia(1:endo_nbr_1,M_.lead_lag_incidence(M_.maximum_lag+1,inst_i));
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% building kmask for z state vector in t+1
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if M_.maximum_lag > 0
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if M_.maximum_lead > 0
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kmask = [flipud(cumsum(M_.lead_lag_incidence(1:M_.maximum_lag,order_var),1))] ;
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kmask = [kmask; flipud(cumsum(flipud(M_.lead_lag_incidence(M_.maximum_lag+2:end,order_var)),1))] ;
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end
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else
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kmask = flipud(cumsum(flipud(M_.lead_lag_incidence(M_.maximum_lag+2:klen,order_var)),1));
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end
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% static variables in objective function
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kmask(1,nstatic+1:nstatic1) = 1;
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kmask = kmask';
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% lags aren't ordered as in dr1 !
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% this is necessary to have the zeros on R's diagonal aligned with jump variables
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kmask = kmask(:);
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i_kmask = find(kmask); % index of nonzero entries in kmask
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nd = size(i_kmask,1); % size of the state vector
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kmask(i_kmask) = [1:nd];
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% auxiliary equations
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% elements that are both in z(t+1) and z(t)
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kad = [];
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kae = [];
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k1 = find([kmask(1:endo_nbr_1) & kmask(M_.maximum_lag*endo_nbr_1+1:(M_.maximum_lag+1)*endo_nbr_1)] );
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if ~isempty(k1)
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kad = kmask(k1);
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kae = kmask(k1+M_.maximum_lag*endo_nbr_1);
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end
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if M_.maximum_lag > 1
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k1 = find([kmask(endo_nbr_1+1:M_.maximum_lag*endo_nbr_1) & kmask(1:(M_.maximum_lag-1)*endo_nbr_1)] );
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if ~isempty(k1)
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kad = [kad; kmask(k1+endo_nbr_1)];
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kae = [kae; kmask(k1)];
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end
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end
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if M_.maximum_lead > 1
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k1 = find([kmask((M_.maximum_lag+1)*endo_nbr_1+1:end) & kmask(M_.maximum_lag*endo_nbr_1+1:end-endo_nbr_1)] );
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if ~isempty(k1)
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kad = [kad; kmask(M_.maximum_lag*endo_nbr_1+k1)];
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kae = [kae; kmask((M_.maximum_lag+1)*endo_nbr_1+k1)];
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end
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end
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% composition of state vector
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% col 1: variable; col 2: lead/lag in z(t+1);
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% col 3: A cols for t+1 (D); col 4: A cols for t (E)
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kstate = [ repmat([1:endo_nbr_1]',klen-1,1) ...
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[kron([M_.maximum_lag+1:-1:2]',ones(endo_nbr_1,1)); ...
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kron([M_.maximum_lag+2:klen]',ones(endo_nbr_1,1))] ...
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zeros((klen-1)*endo_nbr_1,2)];
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kiy = [flipud(M_.lead_lag_incidence(1:M_.maximum_lag+1,order_var)); M_.lead_lag_incidence(M_.maximum_lag+2:end,order_var)]';
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kiy = kiy(:);
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i1 = find(kiy((M_.maximum_lag+1)*endo_nbr_1+1:end));
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kstate(i1+M_.maximum_lag*endo_nbr_1,3) = kiy(i1+(M_.maximum_lag+1)*endo_nbr_1)-M_.endo_nbr;
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kstate(1:M_.maximum_lag*endo_nbr_1,4) = kiy(endo_nbr_1+1:(M_.maximum_lag+1)*endo_nbr_1);
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% put in E only the current variables that are not already in D
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kstate = kstate(i_kmask,:);
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sdyn = M_.endo_nbr-nstatic-inst_nbr;
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% buildind D and E
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d = zeros(nd,nd) ;
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e = d ;
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% dynamical system
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k = find(kstate(:,2) >= M_.maximum_lag+2 & kstate(:,3));
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d(1:sdyn,k) = a(nstatic+1:end,kstate(k,3)) ;
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k1 = find(kstate(:,2) == M_.maximum_lag+2);
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a1 = eye(sdyn);
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e(1:sdyn,k1) = -a1(:,kstate(k1,1)-nstatic);
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k = find(kstate(:,2) <= M_.maximum_lag+1 & kstate(:,4));
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e(1:sdyn,k) = -a(nstatic+1:end,kstate(k,4)) ;
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k2 = find(kstate(:,2) == M_.maximum_lag+1);
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k2 = k2(~ismember(kstate(k2,1),kstate(k1,1)));
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d(1:sdyn,k2) = a1(:,kstate(k2,1)-nstatic);
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if ~isempty(kad)
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for j = 1:size(kad,1)
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d(sdyn+j,kad(j)) = 1 ;
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e(sdyn+j,kae(j)) = 1 ;
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end
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end
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[Q,R] = qr(d);
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C = Q'*[e [[b(nstatic+1:end,:) fu(nstatic+1:end,:)];...
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zeros(nd-sdyn,inst_nbr+M_.exo_nbr)]];
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nyf = sum(kstate(:,2) > M_.maximum_lag+1);
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np = nd - nyf;
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dd = zeros(2*nd+inst_nbr,2*nd+inst_nbr);
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ee = dd;
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dd(1:nd,1:np) = R(:,1:np);
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ee(1:nd,1:np) = C(:,1:np);
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dd(1:nd,nd+1:nd+nyf) = R(:,np+1:end);
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ee(1:nd,nd+1:nd+nyf+inst_nbr) = C(:,np+1:nd+inst_nbr);
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% derivatives with respect to x
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% building QQ and UU
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m = 1;
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v0 = kstate(find(kstate(:,2)==M_.maximum_lag+2),1);
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for i=1:nd;
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if (kstate(i,2) == M_.maximum_lag+1 & isempty(find(kstate(i,1)==v0)))
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k = find(order_var(kstate(i,1))==obj_var);
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if ~isempty(k)
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iq(m) = i;
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m = m+1;
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||||
end
|
||||
elseif kstate(i,2) == M_.maximum_lag+2
|
||||
k = find(order_var(kstate(i,1))==obj_var);
|
||||
if ~isempty(k)
|
||||
iq(m) = i;
|
||||
m = m+1;
|
||||
end
|
||||
end
|
||||
end
|
||||
QQ1 = zeros(nd,nd);
|
||||
QQ1(iq,iq) = W(obj_var,obj_var);
|
||||
UU1 = zeros(nd,inst_nbr);
|
||||
UU1(iq,:) = W(obj_var,inst_i);
|
||||
RR = W(inst_i,inst_i);
|
||||
offset = nd;
|
||||
ee(offset+1:2*offset,1:np) = bet*QQ1(1:np,:)';
|
||||
dd(offset+1:2*offset,np+1:nd) = bet*C(np+1:nd,1:nd)';
|
||||
ee(offset+1:2*offset,nd+1:nd+nyf) = bet*QQ1(np+1:end,:)';
|
||||
dd(offset+1:2*offset,nd+nyf+inst_nbr+1:end)=bet*C(1:np,1:nd)';
|
||||
ee(offset+1:2*offset,np+1:nd) = R(np+1:end,:)';
|
||||
ee(offset+1:2*offset,nd+nyf+1:nd+nyf+inst_nbr) = -bet*UU1;
|
||||
ee(offset+1:2*offset,nd+nyf+inst_nbr+1:end) = R(1:np,:)';
|
||||
|
||||
%derivatives with respect to u
|
||||
offset = 2*nd;
|
||||
dd(offset+1:end,np+1:nd) = -C(np+1:end,nd+1:nd+inst_nbr)';
|
||||
dd(offset+1:end,nd+nyf+inst_nbr+1:end) = -C(1:np,nd+1:nd+inst_nbr)';
|
||||
ee(offset+1:end,1:np) = UU1(1:np,:)';
|
||||
ee(offset+1:end,nd+1:nd+nyf) = UU1(np+1:end,:)';
|
||||
ee(offset+1:end,nd+nyf+1:nd+nyf+inst_nbr) = RR;
|
||||
|
||||
[ss,tt,w,sdim,oo_.eigenvalues,info] = mjdgges(ee,dd);
|
||||
|
||||
nba = sum(abs(oo_.eigenvalues) > 1+1e-5);
|
||||
nyf1 = nd+inst_nbr;
|
||||
nd = 2*nd+inst_nbr;
|
||||
|
||||
if nba ~= nyf1;
|
||||
warning('OLR: Blanchard-Kahn conditions are not satisfied.');
|
||||
end
|
||||
|
||||
np1 = nd - nyf1;
|
||||
n2 = np1 + 1;
|
||||
n3 = np1;
|
||||
n4 = n3 + 1;
|
||||
|
||||
% derivatives with respect to dynamic state variables
|
||||
% forward variables
|
||||
gx = -w(n4:nd,n2:nd)'\w(1:n3,n2:nd)';
|
||||
% predetermined variables
|
||||
hx = w(n4:nd,1:np1)'*gx+w(1:n3,1:np1)';
|
||||
hx = (tt(1:np1,1:np1)*hx)\(ss(1:np1,1:np1)*hx);
|
||||
|
||||
% including Lagrange multipliers in M_.endo_names, order_var and kstate
|
||||
for i=1:M_.maximum_lead
|
||||
k = find(kstate(:,2)==M_.maximum_lag+1+i);
|
||||
temp = strcat('mult_',M_.endo_names(order_var(kstate(k,1)),:));
|
||||
temp = strcat(temp,['_' int2str(i)]);
|
||||
M_.endo_names = strvcat(M_.endo_names,temp);
|
||||
end
|
||||
|
||||
if nyf > nboth
|
||||
order_var = [order_var(1:nstatic+npred+nboth);[M_.endo_nbr+1:M_.endo_nbr+nyf]'; ...
|
||||
order_var(nstatic+npred+nboth+1:end); inst_i];
|
||||
else
|
||||
order_var = [order_var(1:nstatic+npred+nboth);[M_.endo_nbr+1:M_.endo_nbr+nyf]'; ...
|
||||
inst_i];
|
||||
end
|
||||
kstate = [kstate(1:np,:);zeros(nyf,4);kstate(np+1:end,:);zeros(inst_nbr+np,4)];
|
||||
k = find(kstate(:,2) <= M_.maximum_lag+1);
|
||||
kstate(np+1:np+nyf,1:2) = [[nstatic+npred+nboth+1:nstatic+npred+nboth+nyf]' ...
|
||||
(M_.maximum_lag+1)*ones(nyf,1)];
|
||||
kstate(np+2*nyf+1:np+2*nyf+inst_nbr,1:2) = [[endo_nbr_1+1:endo_nbr_1+inst_nbr]' ...
|
||||
(M_.maximum_lag+2)*ones(inst_nbr,1)];
|
||||
kstate(np+2*nyf+inst_nbr+1:end,1:2) = [[M_.endo_nbr+1:M_.endo_nbr+np]' (M_.maximum_lag+2)*ones(np,1)];
|
||||
|
||||
k1 = find(kstate(:,2) == M_.maximum_lag+1);
|
||||
k2 = find(kstate(:,2) == M_.maximum_lag+2)-np-nyf;
|
||||
dr.ghx = [hx(k1,:); gx(k2(nboth+1:end),:)];
|
||||
|
||||
%lead variables actually present in the model
|
||||
% derivatives with respect to exogenous variables
|
||||
if M_.exo_nbr
|
||||
n1 = find(kstate(:,2) > M_.maximum_lag+1);
|
||||
ghu = [dd(:,n1(1):end)*gx+dd(:,1:n1(1)-1) -ee(:,np+nyf+1:end)]\[C(:,end-M_.exo_nbr+1:end); zeros(size(dd,1)-size(C,1),M_.exo_nbr)];
|
||||
dr.ghu = [ghu(k1,:);ghu(k2(nboth+1:end)+np+nyf,:)];
|
||||
end
|
||||
|
||||
% static variables
|
||||
if nstatic > 0
|
||||
j3 = nonzeros(kstate(:,3));
|
||||
j4 = find(kstate(:,3))-np-nyf;
|
||||
temp = -a(1:nstatic,j3)*gx(j4,:)*hx;
|
||||
temp = temp + b(1:nstatic,:)*gx(nyf+1:nyf+inst_nbr,:);
|
||||
j5 = find(kstate(:,4));
|
||||
temp(:,j5) = temp(:,j5)-a(1:nstatic,nonzeros(kstate(:,4)));
|
||||
dr.ghx = [temp; dr.ghx];
|
||||
temp = -a(1:nstatic,j3)*gx(j4,:)*ghu(1:np+nyf,:);
|
||||
temp = temp + b(1:nstatic,:)*ghu(np+2*nyf+1:np+2*nyf+inst_nbr,:);
|
||||
temp = temp + fu(1:nstatic,:);
|
||||
dr.ghu = [temp; dr.ghu];
|
||||
temp = [];
|
||||
end
|
||||
|
||||
dr.ghx = dr.ghx(1:M_.endo_nbr+nyf,:);
|
||||
dr.ghu = dr.ghu(1:M_.endo_nbr+nyf,:);
|
||||
|
||||
dr.ys = [dr.ys; zeros(nyf,1)];
|
||||
dr.nstatic1 = nstatic1;
|
||||
dr.nstatic = nstatic;
|
||||
dr.npred = npred+nyf+nboth;
|
||||
dr.kstate = kstate;
|
||||
dr.order_var = order_var;
|
||||
M_.endo_nbr = M_.endo_nbr+nyf;
|
||||
|
||||
% 05/29/03 MJ replaced diffext by jacobia (much faster)
|
||||
% corrected kmask for static variables in objective function
|
||||
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue