put back DsgeLikelihood_hh.m that is used by option 5 of the optimizer
git-svn-id: https://www.dynare.org/svn/dynare/dynare_v4@1918 ac1d8469-bf42-47a9-8791-bf33cf982152time-shift
michel
parent
9074fdba91
commit
36f264a1ca
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@ -53,8 +53,8 @@ function [LIK, lik] = DiffuseLikelihood1_Z(T,Z,R,Q,Pinf,Pstar,Y,start)
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return
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else
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Fstar = Z*Pstar*Z';
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iFstar = inv(F);
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dFstar = det(F);
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iFstar = inv(Fstar);
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dFstar = det(Fstar);
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Kstar = Pstar*Z'*iFstar;
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lik(t) = log(dFstar) + v'*iFstar*v;
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Pinf = T*Pinf*transpose(T);
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@ -109,7 +109,7 @@ function [LIK, lik] = DiffuseLikelihood1_Z(T,Z,R,Q,Pinf,Pstar,Y,start)
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t = t+1;
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v = Y(:,t)-Z*a;
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a = T*(a+K*v);
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lik(t) = v*iF*v;
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lik(t) = v'*iF*v;
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end
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lik(t) = lik(t) + reste*log(dF);
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@ -151,27 +151,19 @@ function [fval,cost_flag,ys,trend_coeff,info] = DsgeLikelihood(xparam1,gend,data
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%------------------------------------------------------------------------------
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% 3. Initial condition of the Kalman filter
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%------------------------------------------------------------------------------
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kalman_algo = options_.kalman_algo;
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if options_.lik_init == 1 % Kalman filter
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Pstar = lyapunov_symm(T,R*Q*R',options_.qz_criterium);
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Pinf = [];
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kalman_algo = 1;
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Pstar = lyapunov_symm(T,R*Q*R',options_.qz_criterium);
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Pinf = [];
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elseif options_.lik_init == 2 % Old Diffuse Kalman filter
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Pstar = 10*eye(np);
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Pinf = [];
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kalman_algo = 1;
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Pstar = 10*eye(np);
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Pinf = [];
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elseif options_.lik_init == 3 % Diffuse Kalman filter
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if options_.kalman_algo < 4
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Pstar = zeros(np,np);
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ivs = bayestopt_.restrict_var_list_stationary;
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R1 = R(ivs,:);
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Pstar(ivs,ivs) = lyapunov_symm(T(ivs,ivs),R1*Q*R1',options_.qz_criterium);
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% Pinf = bayestopt_.Pinf;
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% by M. Ratto
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RR=T(:,bayestopt_.restrict_var_list_nonstationary);
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i=find(abs(RR)>1.e-10);
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R0=zeros(size(RR));
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R0(i)=sign(RR(i));
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Pinf=R0*R0';
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% by M. Ratto
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else
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if kalman_algo == 1
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kalman_algo == 3
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end
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[QT,ST] = schur(T);
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e1 = abs(ordeig(ST)) > 2-options_.qz_criterium;
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[QT,ST] = ordschur(QT,ST,e1);
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@ -183,104 +175,131 @@ function [fval,cost_flag,ys,trend_coeff,info] = DsgeLikelihood(xparam1,gend,data
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Pstar = zeros(np,np);
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B = QT'*R*Q*R'*QT;
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for i=np:-1:nk+2
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if ST(i,i-1) == 0
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if i == np
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c = zeros(np-nk,1);
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else
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c = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i,i+1:end)')+...
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ST(i,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i);
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end
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q = eye(i-nk)-ST(nk1:i,nk1:i)*ST(i,i);
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Pstar(nk1:i,i) = q\(B(nk1:i,i)+c);
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Pstar(i,nk1:i-1) = Pstar(nk1:i-1,i)';
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else
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if i == np
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c = zeros(np-nk,1);
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c1 = zeros(np-nk,1);
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else
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c = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i,i+1:end)')+...
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ST(i,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i)+...
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ST(i,i-1)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i-1);
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c1 = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i-1,i+1:end)')+...
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ST(i-1,i-1)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i-1)+...
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ST(i-1,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i);
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end
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q = [eye(i-nk)-ST(nk1:i,nk1:i)*ST(i,i) -ST(nk1:i,nk1:i)*ST(i,i-1);...
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-ST(nk1:i,nk1:i)*ST(i-1,i) eye(i-nk)-ST(nk1:i,nk1:i)*ST(i-1,i-1)];
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z = q\[B(nk1:i,i)+c;B(nk1:i,i-1)+c1];
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Pstar(nk1:i,i) = z(1:(i-nk));
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Pstar(nk1:i,i-1) = z(i-nk+1:end);
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Pstar(i,nk1:i-1) = Pstar(nk1:i-1,i)';
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Pstar(i-1,nk1:i-2) = Pstar(nk1:i-2,i-1)';
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i = i - 1;
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end
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if ST(i,i-1) == 0
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if i == np
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c = zeros(np-nk,1);
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else
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c = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i,i+1:end)')+...
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ST(i,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i);
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end
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q = eye(i-nk)-ST(nk1:i,nk1:i)*ST(i,i);
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Pstar(nk1:i,i) = q\(B(nk1:i,i)+c);
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Pstar(i,nk1:i-1) = Pstar(nk1:i-1,i)';
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else
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if i == np
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c = zeros(np-nk,1);
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c1 = zeros(np-nk,1);
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else
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c = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i,i+1:end)')+...
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ST(i,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i)+...
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ST(i,i-1)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i-1);
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c1 = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i-1,i+1:end)')+...
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ST(i-1,i-1)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i-1)+...
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ST(i-1,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i);
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end
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q = [eye(i-nk)-ST(nk1:i,nk1:i)*ST(i,i) -ST(nk1:i,nk1:i)*ST(i,i-1);...
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-ST(nk1:i,nk1:i)*ST(i-1,i) eye(i-nk)-ST(nk1:i,nk1:i)*ST(i-1,i-1)];
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z = q\[B(nk1:i,i)+c;B(nk1:i,i-1)+c1];
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Pstar(nk1:i,i) = z(1:(i-nk));
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Pstar(nk1:i,i-1) = z(i-nk+1:end);
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Pstar(i,nk1:i-1) = Pstar(nk1:i-1,i)';
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Pstar(i-1,nk1:i-2) = Pstar(nk1:i-2,i-1)';
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i = i - 1;
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end
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end
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if i == nk+2
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c = ST(nk+1,:)*(Pstar(:,nk+2:end)*ST(nk1,nk+2:end)')+ST(nk1,nk1)*ST(nk1,nk+2:end)*Pstar(nk+2:end,nk1);
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Pstar(nk1,nk1)=(B(nk1,nk1)+c)/(1-ST(nk1,nk1)*ST(nk1,nk1));
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c = ST(nk+1,:)*(Pstar(:,nk+2:end)*ST(nk1,nk+2:end)')+ST(nk1,nk1)*ST(nk1,nk+2:end)*Pstar(nk+2:end,nk1);
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Pstar(nk1,nk1)=(B(nk1,nk1)+c)/(1-ST(nk1,nk1)*ST(nk1,nk1));
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end
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Z = QT(mf,:);
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R1 = QT'*R;
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end
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[u,s,v]=svd(Z*ST(:,1:nk),0);
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k = find(abs(diag(s)) < 1e-8);
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if length(k) > 0
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[junk,k1] = max(abs(v(:,k)));
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dd =ones(nk,1);
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dd(k1) = zeros(length(k1),1);
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Pinf(1:nk,1:nk) = diag(dd);
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end
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end
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%------------------------------------------------------------------------------
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% 4. Likelihood evaluation
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%------------------------------------------------------------------------------
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if any(any(H ~= 0)) % should be replaced by a flag
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if options_.kalman_algo == 1
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LIK = DiffuseLikelihoodH1(T,R,Q,H,Pinf,Pstar,data,trend,start);
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if isinf(LIK) & ~estim_params_.ncn %% The univariate approach considered here doesn't
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%% apply when H has some off-diagonal elements.
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LIK = DiffuseLikelihoodH3(T,R,Q,H,Pinf,Pstar,data,trend,start);
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elseif isinf(LIK) & estim_params_.ncn
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LIK = DiffuseLikelihoodH3corr(T,R,Q,H,Pinf,Pstar,data,trend,start);
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end
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elseif options_.kalman_algo == 3
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if ~estim_params_.ncn %% The univariate approach considered here doesn't
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%% apply when H has some off-diagonal elements.
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LIK = DiffuseLikelihoodH3(T,R,Q,H,Pinf,Pstar,data,trend,start);
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else
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LIK = DiffuseLikelihoodH3corr(T,R,Q,H,Pinf,Pstar,data,trend,start);
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end
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end
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if kalman_algo == 1
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LIK = DiffuseLikelihoodH1(T,R,Q,H,Pinf,Pstar,data,trend,start);
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if isinf(LIK)
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kalman_algo = 2;
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if ~estim_params_.ncn
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%% The univariate approach considered here doesn't
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%% apply when H has some off-diagonal elements.
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LIK = DiffuseLikelihoodH3(T,R,Q,H,Pinf,Pstar,data,trend,start);
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else
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LIK = DiffuseLikelihoodH3corr(T,R,Q,H,Pinf,Pstar,data,trend,start);
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end
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end
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elseif kalman_algo == 2
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if ~estim_params_.ncn
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%% The univariate approach considered here doesn't
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%% apply when H has some off-diagonal elements.
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LIK = DiffuseLikelihoodH3(T,R,Q,H,Pinf,Pstar,data,trend,start);
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else
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LIK = DiffuseLikelihoodH3corr(T,R,Q,H,Pinf,Pstar,data,trend,start);
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end
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elseif kalman_algo == 3
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data1 = data - trend;
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LIK = DiffuseLikelihoodH1_Z(ST,Z,R1,Q,H,Pinf,Pstar,data1,trend,start);
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if isinf(LIK)
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kalman_algo = 4;
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if ~estim_params_.ncn
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%% The univariate approach considered here doesn't
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%% apply when H has some off-diagonal elements.
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LIK = DiffuseLikelihoodH3_Z(ST,Z,R1,Q,H,Pinf,Pstar,data1,trend,start);
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else
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LIK = DiffuseLikelihoodH3corr_Z(ST,Z,R1,Q,H,Pinf,Pstar,data1,trend,start);
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end
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end
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elseif kalman_algo == 4
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data1 = data - trend;
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if ~estim_params_.ncn
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%% The univariate approach considered here doesn't
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%% apply when H has some off-diagonal elements.
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LIK = DiffuseLikelihoodH3_Z(ST,Z,R1,Q,H,Pinf,Pstar,data1,trend,start);
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else
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LIK = DiffuseLikelihoodH3corr_Z(ST,Z,R1,Q,H,Pinf,Pstar,data1,trend,start);
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end
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end
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else
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if options_.kalman_algo == 1
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%nv = size(bayestopt_.Z,1);
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%LIK = kalman_filter(bayestopt_.Z,zeros(nv,nv),T,R,Q,data,zeros(size(T,1),1),Pstar,'u');
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%tic
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LIK = DiffuseLikelihood1(T,R,Q,Pinf,Pstar,data,trend,start);
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%toc
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%tic
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%LIK1 = Diffuse_Likelihood1(T,R,Q,Pinf,Pstar,data,trend,start);
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%toc
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%LIK1-LIK
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%if abs(LIK1-LIK)>0.0000000001
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% disp(['LIK1 and LIK are not equal! ' num2str(abs(LIK1-LIK))])
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%end
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if isinf(LIK)
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LIK = DiffuseLikelihood3(T,R,Q,Pinf,Pstar,data,trend,start);
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end
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elseif options_.kalman_algo == 3
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LIK = DiffuseLikelihood3(T,R,Q,Pinf,Pstar,data,trend,start);
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elseif options_.kalman_algo == 4
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data1 = data - trend;
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LIK = DiffuseLikelihood1_Z(ST,Z,R1,Q,Pinf,Pstar,data1,start);
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if isinf(LIK)
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LIK = DiffuseLikelihood3_Z(ST,Z,R1,Q,Pinf,Pstar,data1,start);
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end
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elseif options_.kalman_algo == 5
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data1 = data - trend;
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LIK = DiffuseLikelihood3_Z(ST,Z,R1,Q,Pinf,Pstar,data1,start);
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end
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if kalman_algo == 1
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LIK = DiffuseLikelihood1(T,R,Q,Pinf,Pstar,data,trend,start);
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if isinf(LIK)
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kalman_algo = 2
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LIK = DiffuseLikelihood3(T,R,Q,Pinf,Pstar,data,trend,start);
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end
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elseif kalman_algo == 2
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LIK = DiffuseLikelihood3(T,R,Q,Pinf,Pstar,data,trend,start);
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elseif options_.kalman_algo == 3
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data1 = data - trend;
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LIK = DiffuseLikelihood1_Z(ST,Z,R1,Q,Pinf,Pstar,data1,start);
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if isinf(LIK)
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kalman_algo = 4
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LIK = DiffuseLikelihood3_Z(ST,Z,R1,Q,Pinf,Pstar,data1,start);
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end
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elseif kalman_algo == 4
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data1 = data - trend;
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LIK = DiffuseLikelihood3_Z(ST,Z,R1,Q,Pinf,Pstar,data1,start);
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end
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end
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if imag(LIK) ~= 0
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likelihood = bayestopt_.penalty;
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likelihood = bayestopt_.penalty;
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else
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likelihood = LIK;
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likelihood = LIK;
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end
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% ------------------------------------------------------------------------------
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% Adds prior if necessary
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% ------------------------------------------------------------------------------
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lnprior = priordens(xparam1,bayestopt_.pshape,bayestopt_.p1,bayestopt_.p2,bayestopt_.p3,bayestopt_.p4);
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fval = (likelihood-lnprior);
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fval = (likelihood-lnprior);
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options_.kalman_algo = kalman_algo;
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@ -92,27 +92,17 @@ function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK,T,R,P,PK,d,
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Q = M_.Sigma_e;
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H = M_.H;
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kalman_algo = options_.kalman_algo;
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if options_.lik_init == 1 % Kalman filter
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Pstar = lyapunov_symm(T,R*Q*transpose(R),options_.qz_criterium);
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Pinf = [];
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kalman_algo = 1;
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Pstar = lyapunov_symm(T,R*Q*transpose(R),options_.qz_criterium);
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Pinf = [];
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elseif options_.lik_init == 2 % Old Diffuse Kalman filter
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Pstar = 10*eye(np);
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Pinf = [];
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kalman_algo = 1;
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Pstar = 10*eye(np);
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Pinf = [];
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elseif options_.lik_init == 3 % Diffuse Kalman filter
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if options_.kalman_algo < 4
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Pstar = zeros(np,np);
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ivs = bayestopt_.restrict_var_list_stationary;
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R1 = R(ivs,:);
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Pstar(ivs,ivs) = lyapunov_symm(T(ivs,ivs),R1*Q*R1',options_.qz_criterium);
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% Pinf = bayestopt_.Pinf;
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% by M. Ratto
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RR=T(:,bayestopt_.restrict_var_list_nonstationary);
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i=find(abs(RR)>1.e-10);
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R0=zeros(size(RR));
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R0(i)=sign(RR(i));
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Pinf=R0*R0';
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% by M. Ratto
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else
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kalman_algo = 3;
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[QT,ST] = schur(T);
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e1 = abs(ordeig(ST)) > 2-options_.qz_criterium;
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[QT,ST] = ordschur(QT,ST,e1);
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@ -124,99 +114,140 @@ function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK,T,R,P,PK,d,
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Pstar = zeros(np,np);
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B = QT'*R*Q*R'*QT;
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for i=np:-1:nk+2
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if ST(i,i-1) == 0
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if i == np
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c = zeros(np-nk,1);
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else
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c = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i,i+1:end)')+...
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ST(i,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i);
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end
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q = eye(i-nk)-ST(nk1:i,nk1:i)*ST(i,i);
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Pstar(nk1:i,i) = q\(B(nk1:i,i)+c);
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Pstar(i,nk1:i-1) = Pstar(nk1:i-1,i)';
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else
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if i == np
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c = zeros(np-nk,1);
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c1 = zeros(np-nk,1);
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else
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c = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i,i+1:end)')+...
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ST(i,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i)+...
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ST(i,i-1)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i-1);
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c1 = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i-1,i+1:end)')+...
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ST(i-1,i-1)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i-1)+...
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ST(i-1,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i);
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end
|
||||
q = [eye(i-nk)-ST(nk1:i,nk1:i)*ST(i,i) -ST(nk1:i,nk1:i)*ST(i,i-1);...
|
||||
-ST(nk1:i,nk1:i)*ST(i-1,i) eye(i-nk)-ST(nk1:i,nk1:i)*ST(i-1,i-1)];
|
||||
z = q\[B(nk1:i,i)+c;B(nk1:i,i-1)+c1];
|
||||
Pstar(nk1:i,i) = z(1:(i-nk));
|
||||
Pstar(nk1:i,i-1) = z(i-nk+1:end);
|
||||
Pstar(i,nk1:i-1) = Pstar(nk1:i-1,i)';
|
||||
Pstar(i-1,nk1:i-2) = Pstar(nk1:i-2,i-1)';
|
||||
i = i - 1;
|
||||
end
|
||||
if ST(i,i-1) == 0
|
||||
if i == np
|
||||
c = zeros(np-nk,1);
|
||||
else
|
||||
c = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i,i+1:end)')+...
|
||||
ST(i,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i);
|
||||
end
|
||||
q = eye(i-nk)-ST(nk1:i,nk1:i)*ST(i,i);
|
||||
Pstar(nk1:i,i) = q\(B(nk1:i,i)+c);
|
||||
Pstar(i,nk1:i-1) = Pstar(nk1:i-1,i)';
|
||||
else
|
||||
if i == np
|
||||
c = zeros(np-nk,1);
|
||||
c1 = zeros(np-nk,1);
|
||||
else
|
||||
c = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i,i+1:end)')+...
|
||||
ST(i,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i)+...
|
||||
ST(i,i-1)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i-1);
|
||||
c1 = ST(nk1:i,:)*(Pstar(:,i+1:end)*ST(i-1,i+1:end)')+...
|
||||
ST(i-1,i-1)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i-1)+...
|
||||
ST(i-1,i)*ST(nk1:i,i+1:end)*Pstar(i+1:end,i);
|
||||
end
|
||||
q = [eye(i-nk)-ST(nk1:i,nk1:i)*ST(i,i) -ST(nk1:i,nk1:i)*ST(i,i-1);...
|
||||
-ST(nk1:i,nk1:i)*ST(i-1,i) eye(i-nk)-ST(nk1:i,nk1:i)*ST(i-1,i-1)];
|
||||
z = q\[B(nk1:i,i)+c;B(nk1:i,i-1)+c1];
|
||||
Pstar(nk1:i,i) = z(1:(i-nk));
|
||||
Pstar(nk1:i,i-1) = z(i-nk+1:end);
|
||||
Pstar(i,nk1:i-1) = Pstar(nk1:i-1,i)';
|
||||
Pstar(i-1,nk1:i-2) = Pstar(nk1:i-2,i-1)';
|
||||
i = i - 1;
|
||||
end
|
||||
end
|
||||
if i == nk+2
|
||||
c = ST(nk+1,:)*(Pstar(:,nk+2:end)*ST(nk1,nk+2:end)')+ST(nk1,nk1)*ST(nk1,nk+2:end)*Pstar(nk+2:end,nk1);
|
||||
Pstar(nk1,nk1)=(B(nk1,nk1)+c)/(1-ST(nk1,nk1)*ST(nk1,nk1));
|
||||
c = ST(nk+1,:)*(Pstar(:,nk+2:end)*ST(nk1,nk+2:end)')+ST(nk1,nk1)*ST(nk1,nk+2:end)*Pstar(nk+2:end,nk1);
|
||||
Pstar(nk1,nk1)=(B(nk1,nk1)+c)/(1-ST(nk1,nk1)*ST(nk1,nk1));
|
||||
end
|
||||
|
||||
Z = QT(mf,:);
|
||||
R1 = QT'*R;
|
||||
end
|
||||
end
|
||||
% -----------------------------------------------------------------------------
|
||||
% 4. Kalman smoother
|
||||
% -----------------------------------------------------------------------------
|
||||
if any(any(H ~= 0)) % should be replaced by a flag
|
||||
if options_.kalman_algo == 1
|
||||
[alphahat,epsilonhat,etahat,ahat,aK] = DiffuseKalmanSmootherH1(T,R,Q,H,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
|
||||
if all(alphahat(:)==0)
|
||||
[alphahat,epsilonhat,etahat,ahat,aK] = DiffuseKalmanSmootherH3(T,R,Q,H,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
|
||||
end
|
||||
elseif options_.kalman_algo == 3
|
||||
[alphahat,epsilonhat,etahat,ahat,aK] = DiffuseKalmanSmootherH3(T,R,Q,H,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
|
||||
end
|
||||
else
|
||||
if options_.kalman_algo == 1
|
||||
[alphahat,etahat,ahat,aK] = DiffuseKalmanSmoother1(T,R,Q,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
|
||||
if all(alphahat(:)==0)
|
||||
options_.kalman_algo = 3;
|
||||
[alphahat,etahat,ahat,aK] = DiffuseKalmanSmoother3(T,R,Q,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
|
||||
end
|
||||
elseif options_.kalman_algo == 3
|
||||
[alphahat,etahat,ahat,aK] = DiffuseKalmanSmoother3(T,R,Q,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
|
||||
elseif options_.kalman_algo == 4 | options_.kalman_algo == 5
|
||||
data1 = Y - trend;
|
||||
if options_.kalman_algo == 4
|
||||
[alphahat,etahat,ahat,P,aK,PK,d,decomp] = DiffuseKalmanSmoother1_Z(ST, ...
|
||||
Z,R1,Q,Pinf,Pstar,data1,nobs,np,smpl);
|
||||
if any(any(H ~= 0)) % should be replaced by a flag
|
||||
if kalman_algo == 1
|
||||
[alphahat,epsilonhat,etahat,ahat,aK] = ...
|
||||
DiffuseKalmanSmootherH1(T,R,Q,H,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
|
||||
if all(alphahat(:)==0)
|
||||
options_.kalman_algo = 5;
|
||||
[alphahat,etahat,ahat,P,aK,PK,d,decomp] = DiffuseKalmanSmoother3_Z(ST, ...
|
||||
kalman_algo = 2;
|
||||
if ~estim_params.ncn
|
||||
[alphahat,epsilonhat,etahat,ahat,aK] = ...
|
||||
DiffuseKalmanSmootherH3(T,R,Q,H,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
|
||||
else
|
||||
[alphahat,epsilonhat,etahat,ahat,aK] = ...
|
||||
DiffuseKalmanSmootherH3corr(T,R,Q,H,Pinf,Pstar,Y,trend, ...
|
||||
nobs,np,smpl,mf);
|
||||
end
|
||||
end
|
||||
elseif options_.kalman_algo == 2
|
||||
if ~estim_params.ncn
|
||||
[alphahat,epsilonhat,etahat,ahat,aK] = ...
|
||||
DiffuseKalmanSmootherH3(T,R,Q,H,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
|
||||
else
|
||||
[alphahat,epsilonhat,etahat,ahat,aK] = ...
|
||||
DiffuseKalmanSmootherH3corr(T,R,Q,H,Pinf,Pstar,Y,trend, ...
|
||||
nobs,np,smpl,mf);
|
||||
end
|
||||
elseif kalman_algo == 3
|
||||
data1 = Y - trend;
|
||||
[alphahat,epsilonhat,etahat,ahat,aK] = ...
|
||||
DiffuseKalmanSmootherH1_Z(ST,Z,R1,Q,H,Pinf,Pstar,data1,nobs,np,smpl);
|
||||
if all(alphahat(:)==0)
|
||||
kalman_algo = 4;
|
||||
if ~estim_params.ncn
|
||||
[alphahat,epsilonhat,etahat,ahat,aK] = ...
|
||||
DiffuseKalmanSmootherH3_Z(ST,Z,R1,Q,H,Pinf,Pstar,data1,nobs,np,smpl);
|
||||
else
|
||||
[alphahat,epsilonhat,etahat,ahat,aK] = ...
|
||||
DiffuseKalmanSmootherH3corr_Z(ST,Z,R1,Q,H,Pinf,Pstar,data1, ...
|
||||
nobs,np,smpl);
|
||||
end
|
||||
end
|
||||
elseif options_.kalman_algo == 4
|
||||
data1 = Y - trend;
|
||||
if ~estim_params.ncn
|
||||
[alphahat,epsilonhat,etahat,ahat,aK] = ...
|
||||
DiffuseKalmanSmootherH3_Z(ST,Z,R1,Q,H,Pinf,Pstar,data1, ...
|
||||
nobs,np,smpl);
|
||||
else
|
||||
[alphahat,epsilonhat,etahat,ahat,aK] = ...
|
||||
DiffuseKalmanSmootherH3corr_Z(ST,Z,R1,Q,H,Pinf,Pstar,data1, ...
|
||||
nobs,np,smpl);
|
||||
end
|
||||
end
|
||||
else
|
||||
if kalman_algo == 1
|
||||
[alphahat,etahat,ahat,aK] = DiffuseKalmanSmoother1(T,R,Q,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
|
||||
if all(alphahat(:)==0)
|
||||
kalman_algo = 2;
|
||||
[alphahat,etahat,ahat,aK] = DiffuseKalmanSmoother3(T,R,Q,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
|
||||
end
|
||||
elseif kalman_algo == 2
|
||||
[alphahat,etahat,ahat,aK] = DiffuseKalmanSmoother3(T,R,Q,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
|
||||
elseif kalman_algo == 3 | kalman_algo == 4
|
||||
data1 = Y - trend;
|
||||
if options_.kalman_algo == 3
|
||||
[alphahat,etahat,ahat,P,aK,PK,d,decomp] = DiffuseKalmanSmoother1_Z(ST, ...
|
||||
Z,R1,Q,Pinf,Pstar,data1,nobs,np,smpl);
|
||||
if all(alphahat(:)==0)
|
||||
options_.kalman_algo = 4;
|
||||
[alphahat,etahat,ahat,P,aK,PK,d,decomp] = DiffuseKalmanSmoother3_Z(ST, ...
|
||||
Z,R1,Q,Pinf,Pstar, ...
|
||||
data1,nobs,np,smpl);
|
||||
end
|
||||
else
|
||||
[alphahat,etahat,ahat,P,aK,PK,d,decomp] = DiffuseKalmanSmoother3_Z(ST, ...
|
||||
end
|
||||
else
|
||||
[alphahat,etahat,ahat,P,aK,PK,d,decomp] = DiffuseKalmanSmoother3_Z(ST, ...
|
||||
Z,R1,Q,Pinf,Pstar,data1,nobs,np,smpl);
|
||||
end
|
||||
alphahat = QT*alphahat;
|
||||
ahat = QT*ahat;
|
||||
if options_.nk > 0
|
||||
nk = options_.nk;
|
||||
for jnk=1:nk
|
||||
aK(jnk,:,:) = QT*squeeze(aK(jnk,:,:));
|
||||
for i=1:size(PK,4)
|
||||
PK(jnk,:,:,i) = QT*squeeze(PK(jnk,:,:,i))*QT';
|
||||
end
|
||||
for i=1:size(decomp,4)
|
||||
decomp(jnk,:,:,i) = QT*squeeze(decomp(jnk,:,:,i));
|
||||
end
|
||||
end
|
||||
for i=1:size(P,4)
|
||||
P(:,:,i) = QT*squeeze(P(:,:,i))*QT';
|
||||
end
|
||||
end
|
||||
end
|
||||
alphahat = QT*alphahat;
|
||||
ahat = QT*ahat;
|
||||
if options_.nk > 0
|
||||
nk = options_.nk;
|
||||
for jnk=1:nk
|
||||
aK(jnk,:,:) = QT*squeeze(aK(jnk,:,:));
|
||||
for i=1:size(PK,4)
|
||||
PK(jnk,:,:,i) = QT*squeeze(PK(jnk,:,:,i))*QT';
|
||||
end
|
||||
for i=1:size(decomp,4)
|
||||
decomp(jnk,:,:,i) = QT*squeeze(decomp(jnk,:,:,i));
|
||||
end
|
||||
end
|
||||
for i=1:size(P,4)
|
||||
P(:,:,i) = QT*squeeze(P(:,:,i))*QT';
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
|
|
|
|||
|
|
@ -37,6 +37,12 @@ if options_.order > 1
|
|||
options_.order = 1;
|
||||
end
|
||||
|
||||
if (~isempty(options_.unit_root_vars) || options_.diffuse_filter == 1)
|
||||
if options_.lik_init == 1
|
||||
options_.lik_init = 3
|
||||
end
|
||||
end
|
||||
|
||||
if options_.prefilter == 1
|
||||
options_.noconstant = 1;
|
||||
end
|
||||
|
|
|
|||
|
|
@ -99,6 +99,19 @@ if ~(exist('stab_map_','file')==6 | exist('stab_map_','file')==2),
|
|||
end
|
||||
end
|
||||
|
||||
if ~(exist('gsa_sdp','file')==6 | exist('gsa_sdp','file')==2),
|
||||
dynare_root = strrep(which('dynare.m'),'dynare.m','');
|
||||
ss_anova_path = [dynare_root 'gsa/ss_anova_recurs'];
|
||||
if exist(ss_anova_path)
|
||||
addpath(ss_anova_path,path)
|
||||
else
|
||||
disp('Download Dynare sensitivity routines at:')
|
||||
disp('http://eemc.jrc.ec.europa.eu/softwareDYNARE-Dowload.htm')
|
||||
disp(' ' )
|
||||
error('Mapping routines missing!')
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
if options_gsa.morris,
|
||||
options_gsa.redform=1;
|
||||
|
|
|
|||
|
|
@ -104,49 +104,50 @@ function global_initialization()
|
|||
options_.ramsey_policy = 0;
|
||||
|
||||
% estimation
|
||||
options_.load_mh_file = 0;
|
||||
options_.MaxNumberOfBytes = 1e6;
|
||||
options_.MaximumNumberOfMegaBytes = 111;
|
||||
options_.PosteriorSampleSize = 1000;
|
||||
options_.bayesian_irf = 0;
|
||||
options_.bayesian_th_moments = 0;
|
||||
options_.cutoff = 1e-12;
|
||||
options_.diffuse_d = [];
|
||||
options_.diffuse_filter = 0;
|
||||
options_.filter_step_ahead = [];
|
||||
options_.filtered_vars = 0;
|
||||
options_.first_obs = 1;
|
||||
options_.prefilter = 0;
|
||||
options_.presample = 0;
|
||||
options_.kalman_algo = 0;
|
||||
options_.kalman_tol = 1e-12;
|
||||
options_.lik_algo = 1;
|
||||
options_.lik_init = 1;
|
||||
options_.mh_replic = 20000;
|
||||
options_.load_mh_file = 0;
|
||||
options_.load_mh_file = 0;
|
||||
options_.logdata = 0;
|
||||
options_.loglinear = 0;
|
||||
options_.markowitz = 0.5;
|
||||
options_.mh_conf_sig = 0.90;
|
||||
options_.mh_drop = 0.5;
|
||||
options_.mh_jscale = 0.2;
|
||||
options_.mh_init_scale = 2*options_.mh_jscale;
|
||||
options_.mode_file = '';
|
||||
options_.mode_compute = 4;
|
||||
options_.mode_check = 0;
|
||||
options_.prior_trunc = 1e-10;
|
||||
options_.mh_conf_sig = 0.90;
|
||||
options_.mh_mode = 1;
|
||||
options_.mh_nblck = 2;
|
||||
options_.load_mh_file = 0;
|
||||
options_.mh_recover = 0;
|
||||
options_.nodiagnostic = 0;
|
||||
options_.loglinear = 0;
|
||||
options_.unit_root_vars = [];
|
||||
options_.bayesian_irf = 0;
|
||||
options_.bayesian_th_moments = 0;
|
||||
options_.smoother = 0;
|
||||
options_.mh_replic = 20000;
|
||||
options_.mode_check = 0;
|
||||
options_.mode_compute = 4;
|
||||
options_.mode_file = '';
|
||||
options_.moments_varendo = 0;
|
||||
options_.filtered_vars = 0;
|
||||
options_.kalman_algo = 1;
|
||||
options_.kalman_tol = 1e-12;
|
||||
options_.posterior_mode_estimation = 1;
|
||||
options_.MaxNumberOfBytes = 1e6;
|
||||
options_.filter_step_ahead = [];
|
||||
options_.diffuse_d = [];
|
||||
options_.logdata = 0;
|
||||
options_.use_mh_covariance_matrix = 0;
|
||||
options_.noconstant = 0;
|
||||
options_.markowitz = 0.5;
|
||||
options_.nodiagnostic = 0;
|
||||
options_.posterior_mode_estimation = 1;
|
||||
options_.prefilter = 0;
|
||||
options_.presample = 0;
|
||||
options_.prior_trunc = 1e-10;
|
||||
options_.simulation_method = 0;
|
||||
options_.cutoff = 1e-12;
|
||||
options_.smoother = 0;
|
||||
options_.student_degrees_of_freedom = 3;
|
||||
options_.subdraws = [];
|
||||
options_.PosteriorSampleSize = 1000;
|
||||
options_.MaximumNumberOfMegaBytes = 111;
|
||||
options_.unit_root_vars = [];
|
||||
options_.use_mh_covariance_matrix = 0;
|
||||
|
||||
% Misc
|
||||
options_.conf_sig = 0.6;
|
||||
|
|
|
|||
|
|
@ -35,10 +35,10 @@ for k=1:size(s1,1)
|
|||
y = [y; oo_.endo_simul(strmatch(s1(k,:),M_.endo_names,'exact'),:)] ;
|
||||
end
|
||||
|
||||
if options_.smpl == 0
|
||||
if options_.dsample == 0
|
||||
i = [M_.maximum_lag:size(oo_.endo_simul,2)]' ;
|
||||
else
|
||||
i = [options_.smpl(1)+M_.maximum_lag:options_.smpl(2)+M_.maximum_lag]' ;
|
||||
i = [options_.dsample(1)+M_.maximum_lag:options_.dsample(2)+M_.maximum_lag]' ;
|
||||
end
|
||||
|
||||
t = ['Plot of '] ;
|
||||
|
|
@ -75,9 +75,6 @@ elseif rplottype == 2
|
|||
end
|
||||
end
|
||||
|
||||
% 02/28/01 MJ replaced bseastr by MATLAB's strmatch
|
||||
% 06/19/01 MJ added 'exact' to strmatch calls
|
||||
% 06/25/03 MJ correction when options_.smpl ~= 0
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -37,7 +37,19 @@ function steady()
|
|||
homotopy3(options_.homotopy_values, options_.homotopy_steps);
|
||||
end
|
||||
|
||||
steady_;
|
||||
if options_.homotopy_mode == 1
|
||||
homotopy1(options_.homotopy_param,options_.homotopy_exo, ...
|
||||
options_.homotopy_exodet,options_.homotopy_steps);
|
||||
elseif options_.homotopy_mode == 2
|
||||
homotopy2(options_.homotopy_param,options_.homotopy_exo, ...
|
||||
options_.homotopy_exodet,options_.homotopy_steps);
|
||||
elseif options_.homotopy_mode == 3
|
||||
homotopy3(options_.homotopy_param,options_.homotopy_exo, ...
|
||||
options_.homotopy_exodet,options_.homotopy_steps);
|
||||
else
|
||||
steady_;
|
||||
end
|
||||
|
||||
|
||||
disp(' ')
|
||||
disp('STEADY-STATE RESULTS:')
|
||||
|
|
|
|||
Loading…
Reference in New Issue