isolated functions deleted
git-svn-id: https://www.dynare.org/svn/dynare/dynare_v4@1727 ac1d8469-bf42-47a9-8791-bf33cf982152time-shift
assia
parent
065d95c73f
commit
eb1a26962e
255
matlab/dr11.m
255
matlab/dr11.m
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@ -1,255 +0,0 @@
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% Copyright (C) 2001 Michel Juillard
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%
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function dr=dr11(iorder,dr,cheik)
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global M_ options_ oo_
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global it_ stdexo_ means_ dr1_test_ bayestopt_
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% hack for Bayes
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global dr1_test_ bayestopt_
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options_ = set_default_option(options_,'loglinear',0);
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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 = transpose(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 ('Error in model specification: some variables don"t appear as current') ;
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end
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if ~cheik
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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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end
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if M_.maximum_lead > 1 & iorder > 1
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error (['Models with leads on more than one period can only be solved' ...
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' at order 1'])
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end
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dr=set_state_space(dr);
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kstate = dr.kstate;
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kad = dr.kad;
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kae = dr.kae;
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nstatic = dr.nstatic;
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nfwrd = dr.nfwrd;
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npred = dr.npred;
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nboth = dr.nboth;
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order_var = dr.order_var;
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nd = size(kstate,1);
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sdyn = M_.endo_nbr - nstatic;
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tempex = oo_.exo_simul;
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it_ = M_.maximum_lag + 1;
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z = repmat(dr.ys,1,klen);
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z = z(iyr0) ;
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%M_.jacobia=real(diffext('ff1_',[z; oo_.exo_steady_state])) ;
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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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k1 = M_.lead_lag_incidence(find([1:klen] ~= M_.maximum_lag+1),:);
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b = M_.jacobia(:,M_.lead_lag_incidence(M_.maximum_lag+1,order_var));
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a = b\M_.jacobia(:,nonzeros(k1'));
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if any(isinf(a(:)))
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dr1_test_(1) = 5;
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dr1_test_(2) = bayestopt_.penalty;
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end
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if M_.exo_nbr
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fu = b\M_.jacobia(:,nz+1:end);
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end
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if M_.maximum_lead == 0 & M_.maximum_lag == 1; % backward model with one lag
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dr.ghx = -a;
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dr.ghu = -fu;
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return;
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elseif M_.maximum_lead == 0 & M_.maximum_lag > 1 % backward model with lags on more than
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% one period
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e = zeros(endo_nbr,nd);
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k = find(kstate(:,2) <= M_.maximum_lag+1 & kstate(:,4));
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e(:,k) = -a(:,kstate(k,4)) ;
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dr.ghx = e;
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dr.ghu = -fu;
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end
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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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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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options_ = set_default_option(options_,'qz_criterium',1.000001);
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if ~exist('mjdgges')
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% using Chris Sim's routines
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use_qzdiv = 1;
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[ss,tt,qq,w] = qz(e,d);
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[tt,ss,qq,w] = qzdiv(options_.qz_criterium,tt,ss,qq,w);
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ss1=diag(ss);
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tt1=diag(tt);
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warning_state = warning;
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warning off;
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oo_.eigenvalues = ss1./tt1 ;
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warning warning_state;
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nba = nnz(abs(eigval) > options_.qz_criterium);
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else
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use_qzdiv = 0;
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[ss,tt,w,sdim,oo_.eigenvalues,info] = mjdgges(e,d,options_.qz_criterium);
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if info & info ~= nd+2;
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error(['ERROR' info ' in MJDGGES.DLL']);
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end
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nba = nd-sdim;
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end
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nyf = sum(kstate(:,2) > M_.maximum_lag+1);
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if cheik
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dr.rank = rank(w(1:nyf,nd-nyf+1:end));
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% dr.eigval = oo_.eigenvalues;
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return
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end
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eigenvalues = sort(oo_.eigenvalues);
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if nba > nyf;
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% disp('Instability !');
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dr1_test_(1) = 3; %% More eigenvalues superior to unity than forward variables ==> instability.
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dr1_test_(2) = (abs(eigenvalues(nd-nba+1:nd-nyf))-1-1e-5)'*...
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(abs(eigenvalues(nd-nba+1:nd-nyf))-1-1e-5);% Distance to Blanchard-Khan conditions (penalty)
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return
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elseif nba < nyf;
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% disp('Indeterminacy !');
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dr1_test_(1) = 2; %% ==> Indeterminacy.
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dr1_test_(2) = (abs(eigenvalues(nd-nyf+1:nd-nba))-1-1e-5)'*...
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(abs(eigenvalues(nd-nyf+1:nd-nba))-1-1e-5);% Distance to Blanchard-Khan conditions (penality)
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%% warning('DR1: Blanchard-Kahn conditions are not satisfied. Run CHEIK to learn more!');
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return
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end
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np = nd - nyf;
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n2 = np + 1;
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n3 = nyf;
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n4 = n3 + 1;
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% derivatives with respect to dynamic state variables
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% forward variables
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if condest(w(1:n3,n2:nd)) > 1e9
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% disp('Indeterminacy !!');
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dr1_test_(1) = 2;
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dr1_test_(2) = 1;
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return
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end
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warning_state = warning;
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lastwarn('');
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warning off;
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gx = -w(1:n3,n2:nd)'\w(n4:nd,n2:nd)';
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if length(lastwarn) > 0;
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% disp('Indeterminacy !!');
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dr1_test_(1) = 2;
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dr1_test_(2) = 1;
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warning(warning_state);
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return
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end
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% predetermined variables
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hx = w(1:n3,1:np)'*gx+w(n4:nd,1:np)';
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hx = (tt(1:np,1:np)*hx)\(ss(1:np,1:np)*hx);
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lastwarn('');
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if length(lastwarn) > 0;
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% disp('Singularity problem in dr11.m');
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dr1_test_(1) = 2;
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dr1_test_(2) = 1;
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warning(warning_state);
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return
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end
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k1 = find(kstate(n4:nd,2) == M_.maximum_lag+1);
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k2 = find(kstate(1:n3,2) == M_.maximum_lag+2);
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dr.ghx = [hx(k1,:); gx(k2(nboth+1:end),:)];
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%lead variables actually present in the model
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j3 = nonzeros(kstate(:,3));
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j4 = find(kstate(:,3));
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% derivatives with respect to exogenous variables
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if M_.exo_nbr
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a1 = eye(M_.endo_nbr);
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aa1 = [];
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if nstatic > 0
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aa1 = a1(:,1:nstatic);
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end
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dr.ghu = -[aa1 a(:,j3)*gx(j4,1:npred)+a1(:,nstatic+1:nstatic+ ...
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npred) a1(:,nstatic+npred+1:end)]\fu;
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lastwarn('');
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if length(lastwarn) > 0;
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% disp('Singularity problem in dr11.m');
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dr1_test_(1) = 2;
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dr1_test_(2) = 1;
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return
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end
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end
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warning(warning_state);
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% static variables
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if nstatic > 0
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temp = -a(1:nstatic,j3)*gx(j4,:)*hx;
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j5 = find(kstate(n4:nd,4));
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temp(:,j5) = temp(:,j5)-a(1:nstatic,nonzeros(kstate(:,4)));
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dr.ghx = [temp; dr.ghx];
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temp = [];
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end
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if options_.loglinear == 1
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k = find(dr.kstate(:,2) <= M_.maximum_lag+1);
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klag = dr.kstate(k,[1 2]);
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k1 = dr.order_var;
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dr.ghx = repmat(1./dr.ys(k1),1,size(dr.ghx,2)).*dr.ghx.* ...
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repmat(dr.ys(k1(klag(:,1)))',size(dr.ghx,1),1);
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dr.ghu = repmat(1./dr.ys(k1),1,size(dr.ghu,2)).*dr.ghu;
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end
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% necessary when using Sims' routines
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if use_qzdiv
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gx = real(gx);
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hx = real(hx);
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dr.ghx = real(dr.ghx);
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dr.ghu = real(dr.ghu);
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end
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@ -1,6 +0,0 @@
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function e = fbeta(p2,p,p1,perc)
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% must restrict p2 such that a>0 and b>0 ....
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a = (1-p1)*p1^2/p2^2 - p1;
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b = a*(1/p1 - 1);
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e = p - pbeta(perc,a,b);
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function e = fgamma(p2,p,p1,perc)
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b = p2^2/p1;
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a = p1/b;
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e = p - pgamma(perc,a,b);
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function e = figamm(p2,p,p1,perc)
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e = p - pgamma(1/perc,p2/2,2/p1);
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@ -1,24 +0,0 @@
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function ldens = lpdfbeta(x,a,b);
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% function ldens = lpdfbeta(x,a,b)
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% log Beta PDF
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%
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% INPUTS
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% x: density evatuated at x
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% a: Beta distribution paramerer
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% b: Beta distribution paramerer
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%
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% OUTPUTS
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% ldens: the log Beta PDF
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%
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% SPECIAL REQUIREMENTS
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% none
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%
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% part of DYNARE, copyright Dynare Team (2003-2008)
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% Gnu Public License.
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ldens = gammaln(a+b) - gammaln(a) - gammaln(b) + (a-1)*log(x) + (b-1)*log(1-x);
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% 10/11/03 MJ adapted from a GAUSS version by F. Schorfheide, translated
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% to Matlab by R. Wouters.
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% use Matlab gammaln instead of lngam
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@ -1,178 +0,0 @@
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function [abscissa,f,h] = posterior_density_estimate(data,number_of_grid_points,bandwidth,kernel_function)
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%%
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%% This function aims at estimating posterior univariate densities from realisations of a Metropolis-Hastings
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%% algorithm. A kernel density estimator is used (see Silverman [1986]) and the main task of this function is
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%% to obtain an optimal bandwidth parameter.
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%%
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%% * Silverman [1986], "Density estimation for statistics and data analysis".
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%% * M. Skold and G.O. Roberts [2003], "Density estimation for the Metropolis-Hastings algorithm".
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%%
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%% The last section is adapted from Anders Holtsberg's matlab toolbox (stixbox).
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%%
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%% stephane.adjemian@cepremap.cnrs.fr [01/16/2004].
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if size(data,2) > 1 & size(data,1) == 1;
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data = data';
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elseif size(data,2)>1 & size(data,1)>1;
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error('density_estimate: data must be a one dimensional array.');
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end;
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test = log(number_of_grid_points)/log(2);
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if ( abs(test-round(test)) > 10^(-12));
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error('The number of grid points must be a power of 2.');
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end;
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n = size(data,1);
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%% KERNEL SPECIFICATION...
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if strcmp(kernel_function,'gaussian');
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k = inline('inv(sqrt(2*pi))*exp(-0.5*x.^2)');
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k2 = inline('inv(sqrt(2*pi))*(-exp(-0.5*x.^2)+(x.^2).*exp(-0.5*x.^2))'); % second derivate of the gaussian kernel
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k4 = inline('inv(sqrt(2*pi))*(3*exp(-0.5*x.^2)-6*(x.^2).*exp(-0.5*x.^2)+(x.^4).*exp(-0.5*x.^2))'); % fourth derivate...
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k6 = inline('inv(sqrt(2*pi))*(-15*exp(-0.5*x.^2)+45*(x.^2).*exp(-0.5*x.^2)-15*(x.^4).*exp(-0.5*x.^2)+(x.^6).*exp(-0.5*x.^2))'); % sixth derivate...
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mu02 = inv(2*sqrt(pi));
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mu21 = 1;
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elseif strcmp(kernel_function,'uniform');
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k = inline('0.5*(abs(x) <= 1)');
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mu02 = 0.5;
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mu21 = 1/3;
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elseif strcmp(kernel_function,'triangle');
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k = inline('(1-abs(x)).*(abs(x) <= 1)');
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mu02 = 2/3;
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mu21 = 1/6;
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elseif strcmp(kernel_function,'epanechnikov');
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k = inline('0.75*(1-x.^2).*(abs(x) <= 1)');
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mu02 = 3/5;
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mu21 = 1/5;
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elseif strcmp(kernel_function,'quartic');
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k = inline('0.9375*((1-x.^2).^2).*(abs(x) <= 1)');
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mu02 = 15/21;
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mu21 = 1/7;
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elseif strcmp(kernel_function,'triweight');
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k = inline('1.09375*((1-x.^2).^3).*(abs(x) <= 1)');
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k2 = inline('(105/4*(1-x.^2).*x.^2-105/16*(1-x.^2).^2).*(abs(x) <= 1)');
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k4 = inline('(-1575/4*x.^2+315/4).*(abs(x) <= 1)');
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k6 = inline('(-1575/2).*(abs(x) <= 1)');
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mu02 = 350/429;
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mu21 = 1/9;
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elseif strcmp(kernel_function,'cosinus');
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k = inline('(pi/4)*cos((pi/2)*x).*(abs(x) <= 1)');
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k2 = inline('(-1/16*cos(pi*x/2)*pi^3).*(abs(x) <= 1)');
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k4 = inline('(1/64*cos(pi*x/2)*pi^5).*(abs(x) <= 1)');
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k6 = inline('(-1/256*cos(pi*x/2)*pi^7).*(abs(x) <= 1)');
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mu02 = (pi^2)/16;
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mu21 = (pi^2-8)/pi^2;
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end;
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%% OPTIMAL BANDWIDTH PARAMETER....
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if bandwidth == 0; % Rule of thumb bandwidth parameter (Silverman [1986] corrected by
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% Skold and Roberts [2003] for Metropolis-Hastings).
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sigma = std(data);
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h = 2*sigma*(sqrt(pi)*mu02/(12*(mu21^2)*n))^(1/5); % Silverman's optimal bandwidth parameter.
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A = 0;
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for i=1:n;
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j = i;
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while j<= n & data(j,1)==data(i,1);
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j = j+1;
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end;
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A = A + 2*(j-i) - 1;
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end;
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A = A/n;
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h = h*A^(1/5); % correction
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elseif bandwidth == -1; % Adaptation of the Sheather and Jones [1991] plug-in estimation of the optimal bandwidth
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% parameter for metropolis hastings algorithm.
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if strcmp(kernel_function,'uniform') | ...
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strcmp(kernel_function,'triangle') | ...
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strcmp(kernel_function,'epanechnikov') | ...
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strcmp(kernel_function,'quartic');
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error('I can''t compute the optimal bandwidth with this kernel... Try the gaussian, triweight or cosinus kernels.');
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end;
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sigma = std(data);
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A = 0;
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for i=1:n;
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j = i;
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while j<= n & data(j,1)==data(i,1);
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j = j+1;
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end;
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A = A + 2*(j-i) - 1;
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end;
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A = A/n;
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Itilda4 = 8*7*6*5/(((2*sigma)^9)*sqrt(pi));
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g3 = abs(2*A*k6(0)/(mu21*Itilda4*n))^(1/9);
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Ihat3 = 0;
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for i=1:n;
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Ihat3 = Ihat3 + sum(k6((data(i,1)-data)/g3));
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end;
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Ihat3 = -Ihat3/((n^2)*g3^7);
|
||||
g2 = abs(2*A*k4(0)/(mu21*Ihat3*n))^(1/7);
|
||||
Ihat2 = 0;
|
||||
for i=1:n;
|
||||
Ihat2 = Ihat2 + sum(k4((data(i)-data)/g2));
|
||||
end;
|
||||
Ihat2 = Ihat2/((n^2)*g2^5);
|
||||
h = (A*mu02/(n*Ihat2*mu21^2))^(1/5); % equation (22) in Skold and Roberts [2003] --> h_{MH}
|
||||
elseif bandwidth == -2; % Bump killing... We construct local bandwith parameters in order to remove
|
||||
% spurious bumps introduced by long rejecting periods.
|
||||
if strcmp(kernel_function,'uniform') | ...
|
||||
strcmp(kernel_function,'triangle') | ...
|
||||
strcmp(kernel_function,'epanechnikov') | ...
|
||||
strcmp(kernel_function,'quartic');
|
||||
error('I can''t compute the optimal bandwidth with this kernel... Try the gaussian, triweight or cosinus kernels.');
|
||||
end;
|
||||
sigma = std(data);
|
||||
A = 0;
|
||||
T = zeros(n,1);
|
||||
for i=1:n;
|
||||
j = i;
|
||||
while j<= n & data(j,1)==data(i,1);
|
||||
j = j+1;
|
||||
end;
|
||||
T(i) = (j-i);
|
||||
A = A + 2*T(i) - 1;
|
||||
end;
|
||||
A = A/n;
|
||||
Itilda4 = 8*7*6*5/(((2*sigma)^9)*sqrt(pi));
|
||||
g3 = abs(2*A*k6(0)/(mu21*Itilda4*n))^(1/9);
|
||||
Ihat3 = 0;
|
||||
for i=1:n;
|
||||
Ihat3 = Ihat3 + sum(k6((data(i,1)-data)/g3));
|
||||
end;
|
||||
Ihat3 = -Ihat3/((n^2)*g3^7);
|
||||
g2 = abs(2*A*k4(0)/(mu21*Ihat3*n))^(1/7);
|
||||
Ihat2 = 0;
|
||||
for i=1:n;
|
||||
Ihat2 = Ihat2 + sum(k4((data(i)-data)/g2));
|
||||
end;
|
||||
Ihat2 = Ihat2/((n^2)*g2^5);
|
||||
h = ((2*T-1)*mu02/(n*Ihat2*mu21^2)).^(1/5); % Note that h is a column vector (local banwidth parameters).
|
||||
elseif bandwidth > 0;
|
||||
h = bandwidth;
|
||||
else;
|
||||
error('density_estimate: bandwidth must be positive or equal to 0,-1 or -2.');
|
||||
end;
|
||||
|
||||
%% COMPUTE DENSITY ESTIMATE, using the optimal bandwidth parameter.
|
||||
%%
|
||||
%% This section is adapted from Anders Holtsberg's matlab toolbox
|
||||
%% (stixbox --> plotdens.m).
|
||||
|
||||
|
||||
a = min(data) - (max(data)-min(data))/3;
|
||||
b = max(data) + (max(data)-min(data))/3;
|
||||
abscissa = linspace(a,b,number_of_grid_points)';
|
||||
d = abscissa(2)-abscissa(1);
|
||||
xi = zeros(number_of_grid_points,1);
|
||||
xa = (data-a)/(b-a)*number_of_grid_points;
|
||||
for i = 1:n;
|
||||
indx = floor(xa(i));
|
||||
temp = xa(i)-indx;
|
||||
xi(indx+[1 2]) = xi(indx+[1 2]) + [1-temp,temp]';
|
||||
end;
|
||||
xk = [-number_of_grid_points:number_of_grid_points-1]'*d;
|
||||
kk = k(xk/h);
|
||||
kk = kk / (sum(kk)*d*n);
|
||||
f = ifft(fft(fftshift(kk)).*fft([xi ;zeros(size(xi))]));
|
||||
f = real(f(1:number_of_grid_points));
|
||||
Loading…
Reference in New Issue