diff --git a/matlab/DsgeLikelihood_hh.m b/matlab/DsgeLikelihood_hh.m
deleted file mode 100644
index 6cb1c31013172fed3ff7456633d300f7c2e1163a..0000000000000000000000000000000000000000
--- a/matlab/DsgeLikelihood_hh.m
+++ /dev/null
@@ -1,186 +0,0 @@
-function [fval,llik,cost_flag,ys,trend_coeff,info] = DsgeLikelihood_hh(xparam1,gend,data)
-% stephane.adjemian@cepremap.cnrs.fr [09-07-2004]
-%
-% Adapted from mj_optmumlik.m
- global bayestopt_ estim_params_ options_ trend_coeff_ M_ oo_ xparam1_test
-
- fval = [];
- ys = [];
- trend_coeff = [];
- xparam1_test = xparam1;
- cost_flag = 1;
- nobs = size(options_.varobs,1);
- %------------------------------------------------------------------------------
- % 1. Get the structural parameters & define penalties
- %------------------------------------------------------------------------------
- if options_.mode_compute ~= 1 & any(xparam1 < bayestopt_.lb)
- k = find(xparam1 < bayestopt_.lb);
- fval = bayestopt_.penalty+sum((bayestopt_.lb(k)-xparam1(k)).^2);
- llik=fval;
- cost_flag = 0;
- return;
- end
- if options_.mode_compute ~= 1 & any(xparam1 > bayestopt_.ub)
- k = find(xparam1 > bayestopt_.ub);
- fval = bayestopt_.penalty+sum((xparam1(k)-bayestopt_.ub(k)).^2);
- llik=fval;
- cost_flag = 0;
- return;
- end
- Q = M_.Sigma_e;
- for i=1:estim_params_.nvx
- k =estim_params_.var_exo(i,1);
- Q(k,k) = xparam1(i)*xparam1(i);
- end
- offset = estim_params_.nvx;
- if estim_params_.nvn
- H = zeros(nobs,nobs);
- for i=1:estim_params_.nvn
- k = estim_params_.var_endo(i,1);
- H(k,k) = xparam1(i+offset)*xparam1(i+offset);
- end
- offset = offset+estim_params_.nvn;
- end
- if estim_params_.ncx
- for i=1:estim_params_.ncx
- k1 =estim_params_.corrx(i,1);
- k2 =estim_params_.corrx(i,2);
- Q(k1,k2) = xparam1(i+offset)*sqrt(Q(k1,k1)*Q(k2,k2));
- Q(k2,k1) = Q(k1,k2);
- end
- [CholQ,testQ] = chol(Q);
- if testQ %% The variance-covariance matrix of the structural innovations is not definite positive.
- %% We have to compute the eigenvalues of this matrix in order to build the penalty.
- a = diag(eig(Q));
- k = find(a < 0);
- if k > 0
- fval = bayestopt_.penalty+sum(-a(k));
- llik=fval;
- cost_flag = 0;
- return
- end
- end
- offset = offset+estim_params_.ncx;
- end
- if estim_params_.ncn
- for i=1:estim_params_.ncn
- k1 = options_.lgyidx2varobs(estim_params_.corrn(i,1));
- k2 = options_.lgyidx2varobs(estim_params_.corrn(i,2));
- H(k1,k2) = xparam1(i+offset)*sqrt(H(k1,k1)*H(k2,k2));
- H(k2,k1) = H(k1,k2);
- end
- [CholH,testH] = chol(H);
- if testH
- a = diag(eig(H));
- k = find(a < 0);
- if k > 0
- fval = bayestopt_.penalty+sum(-a(k));
- llik=fval;
- cost_flag = 0;
- return
- end
- end
- offset = offset+estim_params_.ncn;
- end
- M_.params(estim_params_.param_vals(:,1)) = xparam1(offset+1:end);
- % for i=1:estim_params_.np
- % M_.params(estim_params_.param_vals(i,1)) = xparam1(i+offset);
- %end
- M_.Sigma_e = Q;
- %------------------------------------------------------------------------------
- % 2. call model setup & reduction program
- %------------------------------------------------------------------------------
- [T,R,SteadyState,info] = dynare_resolve;
- if info(1) == 1 | info(1) == 2 | info(1) == 5
- fval = bayestopt_.penalty+1;
- llik=fval;
- cost_flag = 0;
- return
- elseif info(1) == 3 | info(1) == 4 | info(1) == 20
- fval = bayestopt_.penalty+info(2)^2;
- llik=fval;
- cost_flag = 0;
- return
- end
- if options_.loglinear == 1
- constant = log(SteadyState(bayestopt_.mfys));
- else
- constant = SteadyState(bayestopt_.mfys);
- end
- if bayestopt_.with_trend == 1
- trend_coeff = zeros(nobs,1);
- for i=1:nobs
- trend_coeff(i) = evalin('base',bayestopt_.trend_coeff{i});
- end
- trend = repmat(constant,1,gend)+trend_coeff*[1:gend];
- else
- trend = repmat(constant,1,gend);
- end
- start = options_.presample+1;
- np = size(T,1);
- mf = bayestopt_.mf;
- %------------------------------------------------------------------------------
- % 3. Initial condition of the Kalman filter
- %------------------------------------------------------------------------------
- if options_.lik_init == 1 % Kalman filter
- Pstar = lyapunov_symm(T,R*Q*transpose(R));
- Pinf = [];
- elseif options_.lik_init == 2 % Old Diffuse Kalman filter
- Pstar = 10*eye(np);
- Pinf = [];
- elseif options_.lik_init == 3 % Diffuse Kalman filter
- Pstar = zeros(np,np);
- ivs = bayestopt_.i_T_var_stable;
- Pstar(ivs,ivs) = lyapunov_symm(T(ivs,ivs),R(ivs,:)*Q* ...
- transpose(R(ivs,:)));
- Pinf = bayestopt_.Pinf;
- % by M. Ratto
- RR=T(:,find(~ismember([1:np],ivs)));
- i=find(abs(RR)>1.e-10);
- R0=zeros(size(RR));
- R0(i)=sign(RR(i));
- Pinf=R0*R0';
- % by M. Ratto
- end
- %------------------------------------------------------------------------------
- % 4. Likelihood evaluation
- %------------------------------------------------------------------------------
- if estim_params_.nvn
- if options_.kalman_algo == 1
- [LIK, lik] = DiffuseLikelihoodH1(T,R,Q,H,Pinf,Pstar,data,trend,start);
- if isinf(LIK) & ~estim_params_.ncn %% The univariate approach considered here doesn't
- %% apply when H has some off-diagonal elements.
- [LIK, lik] = DiffuseLikelihoodH3(T,R,Q,H,Pinf,Pstar,data,trend,start);
- elseif isinf(LIK) & estim_params_.ncn
- [LIK, lik] = DiffuseLikelihoodH3corr(T,R,Q,H,Pinf,Pstar,data,trend,start);
- end
- elseif options_.kalman_algo == 3
- if ~estim_params_.ncn %% The univariate approach considered here doesn't
- %% apply when H has some off-diagonal elements.
- [LIK, lik] = DiffuseLikelihoodH3(T,R,Q,H,Pinf,Pstar,data,trend,start);
- else
- [LIK, lik] = DiffuseLikelihoodH3corr(T,R,Q,H,Pinf,Pstar,data,trend,start);
- end
- end
- else
- if options_.kalman_algo == 1
- [LIK, lik] = DiffuseLikelihood1(T,R,Q,Pinf,Pstar,data,trend,start);
- if isinf(LIK)
- [LIK, lik] = DiffuseLikelihood3(T,R,Q,Pinf,Pstar,data,trend,start);
- end
- elseif options_.kalman_algo == 3
- [LIK, lik] = DiffuseLikelihood3(T,R,Q,Pinf,Pstar,data,trend,start);
- end
- end
- if imag(LIK) ~= 0
- likelihood = bayestopt_.penalty;
- lik=ones(size(lik)).*bayestopt_.penalty;
- else
- likelihood = LIK;
- end
- % ------------------------------------------------------------------------------
- % Adds prior if necessary
- % ------------------------------------------------------------------------------
- lnprior = priordens(xparam1,bayestopt_.pshape,bayestopt_.p1,bayestopt_.p2,bayestopt_.p3,bayestopt_.p4);
- fval = (likelihood-lnprior);
- llik=[-lnprior; .5*lik(start:end)];
diff --git a/matlab/check_model.m b/matlab/check_model.m
index 7189ca24884df8875992b2a738bcfa1d04d5b773..c7e2610438dc1c83f8e1b9737c5197cb20921f52 100644
--- a/matlab/check_model.m
+++ b/matlab/check_model.m
@@ -1,8 +1,8 @@
function check_model()
- global iy_ ykmin_ ykmax_ xkmin_ xkmax_ iy_ exo_det_nbr
+ global M_
- xlen = xkmin_ + xkmax_ + 1;
- if ~ iy_(ykmin_+1,:) > 0
+ xlen = M_.maximum_exo_lag+M_.maximum_exo_lead + 1;
+ if ~ M_.lead_lag_incidence(M_.maximum_lag+1,:) > 0
error ('RESOL: Error in model specification: some variables don"t appear as current') ;
end
@@ -11,7 +11,7 @@ if xlen > 1
' current period. Use additional endogenous variables']) ;
end
-if (exo_det_nbr > 0) & (ykmin_ > 1 | ykmax_ > 1)
+if (M_.exo_det_nbr > 0) & (M_.maximum_lag > 1 | M_.maximum_lead > 1)
error(['Exogenous deterministic variables are currently only allowed in' ...
' models with leads and lags on only one period'])
end
diff --git a/matlab/dr1.m b/matlab/dr1.m
index d6c159e16dc7d0f115d741537667ac954fe6ba77..0a2fb2b1b288fcca2c28bd56a77dab3560e04972 100644
--- a/matlab/dr1.m
+++ b/matlab/dr1.m
@@ -45,7 +45,9 @@ z = z(iyr0) ;
if options_.order == 1
[junk,jacobia_] = feval([M_.fname '_dynamic'],z,tempex);
elseif options_.order == 2
- [junk,jacobia_,hessian] = feval([M_.fname '_dynamic'],z,tempex);
+ [junk,jacobia_,hessian] = feval([M_.fname '_dynamic'],z,...
+ [oo_.exo_simul ...
+ oo_.exo_det_simul]);
end
oo_.exo_simul = tempex ;
@@ -182,7 +184,7 @@ if any(isinf(a(:)))
return
end
if M_.exo_nbr
- fu = aa(:,nz+1:end);
+ fu = aa(:,nz+(1:M_.exo_nbr));
end
clear aa;
@@ -317,15 +319,15 @@ if use_qzdiv
end
%exogenous deterministic variables
-if M_.exo_det_nbr > 1
+if M_.exo_det_nbr > 0
f1 = sparse(jacobia_(:,nonzeros(M_.lead_lag_incidence(M_.maximum_lag+2:end,order_var))));
f0 = sparse(jacobia_(:,nonzeros(M_.lead_lag_incidence(M_.maximum_lag+1,order_var))));
fudet = sparse(jacobia_(:,nz+M_.exo_nbr+1:end));
M1 = inv(f0+[zeros(M_.endo_nbr,nstatic) f1*gx zeros(M_.endo_nbr,nyf-nboth)]);
M2 = M1*f1;
- dr.ghud = cell(M_.oo_.exo_simuldet_length,1);
+ dr.ghud = cell(M_.exo_det_length,1);
dr.ghud{1} = -M1*fudet;
- for i = 2:M_.oo_.exo_simuldet_length
+ for i = 2:M_.exo_det_length
dr.ghud{i} = -M2*dr.ghud{i-1}(end-nyf+1:end,:);
end
end
@@ -344,14 +346,14 @@ if M_.maximum_lag > 0
end
kk = kk';
kk = find(kk(:));
-nk = size(kk,1)+M_.exo_nbr;
+nk = size(kk,1) + M_.exo_nbr + M_.exo_det_nbr;
k1 = M_.lead_lag_incidence(:,order_var);
k1 = k1';
k1 = k1(:);
k1 = k1(kk);
k2 = find(k1);
kk1(k1(k2)) = k2;
-kk1 = [kk1 length(k1)+1:length(k1)+M_.exo_nbr];
+kk1 = [kk1 length(k1)+1:length(k1)+M_.exo_nbr+M_.exo_det_nbr];
kk = reshape([1:nk^2],nk,nk);
kk1 = kk(kk1,kk1);
%[junk,junk,hessian] = feval([M_.fname '_dynamic'],z, oo_.exo_steady_state);
@@ -384,8 +386,8 @@ for i=1:M_.maximum_lead
kk = kk(:,k0);
k0 = k1;
end
-zx=[zx; zeros(M_.exo_nbr,np)];
-zu=[zu; eye(M_.exo_nbr)];
+zx=[zx; zeros(M_.exo_nbr,np);zeros(M_.exo_det_nbr,np)];
+zu=[zu; eye(M_.exo_nbr);zeros(M_.exo_det_nbr,M_.exo_nbr)];
[n1,n2] = size(zx);
if n1*n1*n2*n2 > 1e7
rhs = zeros(M_.endo_nbr,n2*n2);
@@ -571,33 +573,33 @@ if M_.exo_det_nbr > 0
hud = dr.ghud{1}(nstatic+1:nstatic+npred,:);
zud=[zeros(np,M_.exo_det_nbr);dr.ghud{1};gx(:,1:npred)*hud;zeros(M_.exo_nbr,M_.exo_det_nbr);eye(M_.exo_det_nbr)];
R1 = hessian*kron(zx,zud);
- dr.ghxud = cell(M_.oo_.exo_simuldet_length,1);
+ dr.ghxud = cell(M_.exo_det_length,1);
kf = [M_.endo_nbr-nyf+1:M_.endo_nbr];
kp = nstatic+[1:npred];
dr.ghxud{1} = -M1*(R1+f1*dr.ghxx(kf,:)*kron(dr.ghx(kp,:),dr.ghud{1}(kp,:)));
Eud = eye(M_.exo_det_nbr);
- for i = 2:M_.oo_.exo_simuldet_length
+ for i = 2:M_.exo_det_length
hudi = dr.ghud{i}(kp,:);
zudi=[zeros(np,M_.exo_det_nbr);dr.ghud{i};gx(:,1:npred)*hudi;zeros(M_.exo_nbr+M_.exo_det_nbr,M_.exo_det_nbr)];
R2 = hessian*kron(zx,zudi);
dr.ghxud{i} = -M2*(dr.ghxud{i-1}(kf,:)*kron(hx,Eud)+dr.ghxx(kf,:)*kron(dr.ghx(kp,:),dr.ghud{i}(kp,:)))-M1*R2;
end
R1 = hessian*kron(zu,zud);
- dr.ghudud = cell(M_.oo_.exo_simuldet_length,1);
+ dr.ghudud = cell(M_.exo_det_length,1);
kf = [M_.endo_nbr-nyf+1:M_.endo_nbr];
dr.ghuud{1} = -M1*(R1+f1*dr.ghxx(kf,:)*kron(dr.ghu(kp,:),dr.ghud{1}(kp,:)));
Eud = eye(M_.exo_det_nbr);
- for i = 2:M_.oo_.exo_simuldet_length
+ for i = 2:M_.exo_det_length
hudi = dr.ghud{i}(kp,:);
zudi=[zeros(np,M_.exo_det_nbr);dr.ghud{i};gx(:,1:npred)*hudi;zeros(M_.exo_nbr+M_.exo_det_nbr,M_.exo_det_nbr)];
R2 = hessian*kron(zu,zudi);
dr.ghuud{i} = -M2*dr.ghxud{i-1}(kf,:)*kron(hu,Eud)-M1*R2;
end
R1 = hessian*kron(zud,zud);
- dr.ghudud = cell(M_.oo_.exo_simuldet_length,M_.oo_.exo_simuldet_length);
+ dr.ghudud = cell(M_.exo_det_length,M_.exo_det_length);
dr.ghudud{1,1} = -M1*R1-M2*dr.ghxx(kf,:)*kron(hud,hud);
- for i = 2:M_.oo_.exo_simuldet_length
+ for i = 2:M_.exo_det_length
hudi = dr.ghud{i}(nstatic+1:nstatic+npred,:);
zudi=[zeros(np,M_.exo_det_nbr);dr.ghud{i};gx(:,1:npred)*hudi+dr.ghud{i-1}(kf,:);zeros(M_.exo_nbr+M_.exo_det_nbr,M_.exo_det_nbr)];
R2 = hessian*kron(zudi,zudi);
diff --git a/matlab/dynare_m.exe b/matlab/dynare_m.exe
index 3fbf7fe8a894519b00d7a92ce71eab66d7b3ad37..eaada2ce5eeb321f06c007de29b9ddffa8de30b8 100755
Binary files a/matlab/dynare_m.exe and b/matlab/dynare_m.exe differ
diff --git a/matlab/hessian2.m b/matlab/hessian2.m
deleted file mode 100644
index e113b8f8c61e0a6c9b1a4f00be9c9534e6b4bbb3..0000000000000000000000000000000000000000
--- a/matlab/hessian2.m
+++ /dev/null
@@ -1,511 +0,0 @@
-function hessian2(xparam1,gend,data)
-% source Schorfheide
-%/* filename: lbdhess.g
-%** description: The program computes the hessianfit at the posterior mode
-%** created: 05/05/00
-%/********************************************************
-%/* Compute hessianfit, element by element, fine tune with dxscale
-% Compute hessianfit for two step-seize (dx) and take average to prevent singularity
-%/********************************************************
-npara = length(xparam1);
-para = xparam1;
-ndx = 1;%6
-%dx = exp(-seqa(6,2,ndx));
-%dx = exp([-6:-2:-16]);
-dx = exp([-10]);
-hessianfit = zeros( npara, npara );
-gradx = zeros(ndx,1);
-grady = zeros(ndx,1);
-gradxy = zeros(ndx,1);
-hessdiag = zeros(ndx,1);
-dxscale = ones(npara,1);
-% dxscale(5,1)=10;
-% dxscale(13,1)=10;
-% dxscale(17,1)=10;
-% dxscale(8,1)=.10;
-% dxscale(11,1)=.10;
-% dxscale(31,1)=.10;
-
-
-%/* Compute Diagonal elements first
-%*/
-seli = 1;
-fx = mj_optmumlik(para,gend,data,1);
-%do until seli > npara;
-while seli <= npara;
-% locate 1,1;
-% "hessianfit Element (" seli seli ")";
- i=1;
- while i <= ndx;
- paradx = para;
- parady = para;
- paradx(seli) = paradx(seli) + dx(i)*dxscale(seli);
- parady(seli) = parady(seli) - dx(i)*dxscale(seli);
- paradxdy = paradx;
- paradxdy(seli) = paradxdy(seli) - dx(i)*dxscale(seli);
-% fx = optmumlik20(para,q,h2,aa,mf,gitno,gst,gobs,gend,gstart,nlags,rawdata,lpd);
-fdx = mj_optmumlik(paradx,gend,data,1);
-% fdx = optmumlik20(paradx,q,h2,aa,mf,gitno,gst,gobs,gend,gstart,nlags,rawdata,lpd);
-fdy = mj_optmumlik(parady,gend,data,1);
- % fdy = optmumlik20(parady,q,h2,aa,mf,gitno,gst,gobs,gend,gstart,nlags,rawdata,lpd);
-% fdxdy = optmumlik20(paradxdy,q,h2,aa,mf,gitno,gst,gobs,gend,gstart,nlags,rawdata,lpd);
- fdxdy = fx;
- gradx(i) = -( fx - fdx )/ (dx(i)*dxscale(seli));
- grady(i) = ( fx - fdy )/ (dx(i)*dxscale(seli));
- gradxy(i) = -(fx -fdxdy)/ sqrt( (dx(i)*dxscale(seli))^2 + (dx(i)*dxscale(seli))^2 );
- hessdiag(i) = -( 2*fx - fdx - fdy)/(dx(i)*dxscale(seli))^2;
- hessdiag(i) = -( fx - fdx - fdy + fdxdy )/(dx(i)*dx(i)*dxscale(seli)*dxscale(seli));
- i = i+1;
- end;
-% "Values";
-% -hessdiag';
- hessianfit(seli,seli) = -1*(hessdiag(1));
-% hessianfit(seli,seli) = -0.5*(hessdiag(3)+hessdiag(4));
-% locate 6,1;
-% "Value Used:" hessianfit[seli,seli];
- seli = seli+1
-end;
-
-diag(hessianfit)
-
-%/* Now compute off-diagonal elements
-%** Make sure that correlations are between -1 and 1
-%** errorij contains the index of elements that are invalid
-%*/
-errorij = [ 0 0 0];
-
-seli = 1;
-for seli = 1:npara;
-% selj = seli+1;
- for selj =seli+1:npara;
- disp([seli selj]);
-% locate 1,1;
-% "hessianfit Element (" seli selj ")";
- i=1;
- while i <= ndx;
- paradx = para;
- parady = para;
- paradx(seli) = paradx(seli) + dx(i)*dxscale(seli);
- parady(selj) = parady(selj) - dx(i)*dxscale(selj);
- paradxdy = paradx;
- paradxdy(selj) = paradxdy(selj) - dx(i)*dxscale(selj);
-% fx = optmumlik20(para,q,h2,aa,mf,gitno,gst,gobs,gend,gstart,nlags,rawdata,lpd);
-fdx = mj_optmumlik(paradx,gend,data,1);
-% fdx = optmumlik20(paradx,q,h2,aa,mf,gitno,gst,gobs,gend,gstart,nlags,rawdata,lpd);
-fdy = mj_optmumlik(parady,gend,data,1);
-% fdy = optmumlik20(parady,q,h2,aa,mf,gitno,gst,gobs,gend,gstart,nlags,rawdata,lpd);
-fdy = mj_optmumlik(paradxdy,gend,data,1);
-% fdxdy = optmumlik20(paradxdy,q,h2,aa,mf,gitno,gst,gobs,gend,gstart,nlags,rawdata,lpd);
- gradx(i) = -( fx - fdx )/ (dx(i)*dxscale(seli));
- grady(i) = ( fx - fdy )/ (dx(i)*dxscale(selj));
- gradxy(i) = -(fx -fdxdy)/ sqrt( (dx(i)*dxscale(selj))^2 + (dx(i)*dxscale(seli))^2 );
- hessdiag(i) = -( 2*fx - fdx - fdy)/(dx(i)*dxscale(seli))^2;
- hessdiag(i) = -( fx - fdx - fdy + fdxdy )/(dx(i)*dx(i)*dxscale(seli)*dxscale(selj));
- i = i+1;
- end;
-% "Values";
-% -hessdiag';
-
-% hessianfit(seli,selj) = -0.5*(hessdiag(3)+hessdiag(4));
- hessianfit(seli,selj) = -1*(hessdiag(1));
-
- if ( hessianfit(seli,seli) == 0 ) | (hessianfit(selj,selj) == 0);
- corrij = 0;
- else;
- corrij = hessianfit(seli,selj)/sqrt(hessianfit(seli,seli)*hessianfit(selj,selj));
- end;
-
- if (corrij < -1) | (corrij > 1);
- hessianfit(seli,selj)=0;
- errorij = [ errorij [seli selj corrij] ];
- end;
- hessianfit(selj,seli) = hessianfit(seli,selj);
-
-% locate 6,1;
-% "Value Used: " hessianfit[seli,selj];
-% "Correlation:" corrij;
-% "Number of Errors:" rows(errorij)-1;
-% selj=selj+1;
- end;
-% seli = seli+1;
-end;
-
-%cls;
-disp('Errors')
-disp(errorij);
-
-
-%/*******************************************************************************
-
-bbbb=xparam1;
-% func =fval;
-% grad=grad;
-% retcode=exitflag
- opfhessfit = (-hessianfit);
- invhess=inv(opfhessfit);
- stdh=sqrt(diag(invhess));
- pr =length(xparam1);
- tstath=zeros(pr,1);
- i = 1; while i <= pr ; %do until i>pr;
- tstath(i)=abs(bbbb(i))/stdh(i);
- i=i+1; end ; %endo;
-%tstath
-% print "t-stats. from the Hessian";
- disp('print "t-stats. from the Hessian" ') ;
- disp([xparam1 stdh tstath]);
-
-bbbb=xparam1;
-% func =fval;
-% grad=grad;
-% retcode=exitflag
- % opfhessfit = (-hessianfit);
- % invhess=inv(opfhessfit*.5+opfhess*.5);
- % stdh=sqrt(diag(invhess));
- % pr =length(xparam1);
- % tstath=zeros(pr,1);
- % i = 1; while i <= pr ; %do until i>pr;
- % tstath(i)=abs(bbbb(i))/stdh(i);
- % i=i+1; end ; %endo;
-%tstath
-% print "t-stats. from the Hessian";
- % disp('print "t-stats. from the Hessian" ') ;
-% disp([xparam1 stdh tstath]);
-
-%opfhessfit = -hessianfit*.5+opfhess*.5;
-hessian=opfhessfit;
-
-
-
-return;
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-npara = length(xparam1);
-para = xparam1;
-ndx = 1;%6
-%dx = exp(-seqa(6,2,ndx));
-%dx = exp([-6:-2:-16]);
-dx = exp([-10]);
-hessianfit = zeros( npara, npara );
-gradx = zeros(ndx,1);
-grady = zeros(ndx,1);
-gradxy = zeros(ndx,1);
-hessdiag = zeros(ndx,1);
-dxscale = ones(npara,1);
-
-
-%/* Compute Diagonal elements first
-%*/
-seli = 1;
-%do until seli > npara;
-while seli <= npara;
-% locate 1,1;
-% "hessianfit Element (" seli seli ")";
- i=1;
- while i <= ndx;
- paradx = para;
- parady = para;
- paradx(seli) = paradx(seli) + dx(i)*dxscale(seli);
- parady(seli) = parady(seli) - dx(i)*dxscale(seli);
- paradxdy = paradx;
- paradxdy(seli) = paradxdy(seli) - dx(i)*dxscale(seli);
- fx = optmumlik20(para,q,h2,aa,mf,gitno,gst,gobs,gend,gstart,nlags,rawdata,lpd);
- fdx = optmumlik20(paradx,q,h2,aa,mf,gitno,gst,gobs,gend,gstart,nlags,rawdata,lpd);
- fdy = optmumlik20(parady,q,h2,aa,mf,gitno,gst,gobs,gend,gstart,nlags,rawdata,lpd);
- fdxdy = optmumlik20(paradxdy,q,h2,aa,mf,gitno,gst,gobs,gend,gstart,nlags,rawdata,lpd);
- gradx(i) = -( fx - fdx )/ (dx(i)*dxscale(seli));
- grady(i) = ( fx - fdy )/ (dx(i)*dxscale(seli));
- gradxy(i) = -(fx -fdxdy)/ sqrt( (dx(i)*dxscale(seli))^2 + (dx(i)*dxscale(seli))^2 );
- hessdiag(i) = -( 2*fx - fdx - fdy)/(dx(i)*dxscale(seli))^2;
- hessdiag(i) = -( fx - fdx - fdy + fdxdy )/(dx(i)*dx(i)*dxscale(seli)*dxscale(seli));
- i = i+1;
- end;
-% "Values";
-% -hessdiag';
- hessianfit(seli,seli) = -1*(hessdiag(1));
-% hessianfit(seli,seli) = -0.5*(hessdiag(3)+hessdiag(4));
-% locate 6,1;
-% "Value Used:" hessianfit[seli,seli];
- seli = seli+1;
-end;
-
-%/* Now compute off-diagonal elements
-%** Make sure that correlations are between -1 and 1
-%** errorij contains the index of elements that are invalid
-%*/
-errorij = [ 0 0 0];
-
-seli = 1;
-for seli = 1:npara;
- selj = seli+1
- while selj <= npara;
-% locate 1,1;
-% "hessianfit Element (" seli selj ")";
- i=1;
- while i <= ndx;
- paradx = para;
- parady = para;
- paradx(seli) = paradx(seli) + dx(i)*dxscale(seli);
- parady(selj) = parady(selj) - dx(i)*dxscale(selj);
- paradxdy = paradx;
- paradxdy(selj) = paradxdy(selj) - dx(i)*dxscale(selj);
- fx = optmumlik20(para,q,h2,aa,mf,gitno,gst,gobs,gend,gstart,nlags,rawdata,lpd);
- fdx = optmumlik20(paradx,q,h2,aa,mf,gitno,gst,gobs,gend,gstart,nlags,rawdata,lpd);
- fdy = optmumlik20(parady,q,h2,aa,mf,gitno,gst,gobs,gend,gstart,nlags,rawdata,lpd);
- fdxdy = optmumlik20(paradxdy,q,h2,aa,mf,gitno,gst,gobs,gend,gstart,nlags,rawdata,lpd);
- gradx(i) = -( fx - fdx )/ (dx(i)*dxscale(seli));
- grady(i) = ( fx - fdy )/ (dx(i)*dxscale(selj));
- gradxy(i) = -(fx -fdxdy)/ sqrt( (dx(i)*dxscale(selj))^2 + (dx(i)*dxscale(seli))^2 );
- hessdiag(i) = -( 2*fx - fdx - fdy)/(dx(i)*dxscale(seli))^2;
- hessdiag(i) = -( fx - fdx - fdy + fdxdy )/(dx(i)*dx(i)*dxscale(seli)*dxscale(selj));
- i = i+1
- end;
-% "Values";
-% -hessdiag';
-
-% hessianfit(seli,selj) = -0.5*(hessdiag(3)+hessdiag(4));
- hessianfit(seli,selj) = -1*(hessdiag(1));
-
- if ( hessianfit(seli,seli) == 0 ) or (hessianfit(selj,selj) == 0);
- corrij = 0;
- else;
- corrij = hessianfit(seli,selj)/sqrt(hessianfit(seli,seli)*hessianfit(selj,selj));
- end;
-
- if (corrij < -1) or (corrij > 1);
- hessianfit(seli,selj)=0;
- errorij = [ errorij [seli selj corrij] ];
- end;
- hessianfit(selj,seli) = hessianfit(seli,selj);
-
-% locate 6,1;
-% "Value Used: " hessianfit[seli,selj];
-% "Correlation:" corrij;
-% "Number of Errors:" rows(errorij)-1;
- selj=selj+1
- end;
-% seli = seli+1;
-end;
-
-%cls;
-disp('Errors')
-disp(errorij);
-
-
-%/*******************************************************************************
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-%new;
-%closeall;
-%library user, pgraph, lbdlib;
-%format /mb1 /ros 16,8;
-%cls;
-
-%/********************************************************
-%** Estimate the DSGE Model
-%** Models: 1 = RBC
-%** 2 = LBD
-%** 3 = LBD + Effort
-
-%mspec = 3;
-%mprior = 2;
-%npara = 12;
-
-%/********************************************************
-%** Import data on output growth and inflation: series (nobs,2)
-%** observations from 1954:III to 1997:IV:
-%**
-%** YY is composed of gdpq_cld and blsh_cl
-%*/
-%#include c:\projects\active\persist\Gauss\prog_t03\loaddata.g
-%loadm path = ^lpath para_names;
-%
-%/********************************************************
-%** Load Posterior Mode Estimates
-
-%lpara = lpath $+ "\\" $+ lmodel $+ lprior $+ "mode";
-%open fhpara = ^lpara for read;
-%para = readr(fhpara,npara);
-%closeall fhpara;
-
-%"Parameter | Estimate ";
-%outmat = para_names'~para;
-%let mask[1,2] = 0 1;
-%%let fmt[2,3] =
-% "-*.*s " 10 4
-% "*.*lf " 10 4;
-%d = printfm(outmat,(0 ~ 1),fmt);
-
-%"";
-%"Prior*Likelihood at Mode";
-%fcn(para);
-%"Press Key to Continue";
-%k = keyw;
-%cls;
-% $$$
-% $$$ /*
-% $$$ goto evalhess;
-% $$$ */
-% $$$
-% $$$
-% $$$
-% $$$
-% $$$
-% $$$
-% $$$
-% $$$
-% $$$
-% $$$
-% $$$
-% $$$
-% $$$
-% $$$
-% $$$
-% $$$ /* Initialize Output files
-% $$$ */
-% $$$ ohess = lpath $+ "\\" $+ lmodel $+ lprior $+ "hess";
-% $$$ create fhhess=^ohess with hess, npara, 8;
-% $$$ wr = writer(fhhess,hessianfit[1:npara,1:npara]);
-% $$$ closeall fhhess;
-% $$$
-% $$$
-% $$$
-% $$$
-% $$$ /* Load hessianfit, compute penalty
-% $$$ */
-% $$$ evalhess:
-% $$$
-% $$$ lhess = lpath $+ "\\" $+ lmodel $+ lprior $+ "hess";
-% $$$ open fhhess = ^lhess for read;
-% $$$ HHm = readr( fhhess,npara );
-% $$$ closeall fhhess;
-% $$$
-% $$$ /* hessianfit is of reduced rank. Keep zero rows and columns and do SVD
-% $$$ */
-% $$$ if mspec == 1;
-% $$$ rankHHm = 9;
-% $$$ elseif mspec == 2;
-% $$$ rankHHm = 11;
-% $$$ else;
-% $$$ rankHHm = 12;
-% $$$ endif;
-% $$$
-% $$$ /* Create Inverse by Singular Value Decomposition
-% $$$ */
-% $$$ {u , s, v} = svd1(HHM);
-% $$$ invHHMdet = 1;
-% $$$
-% $$$ i = 1;
-% $$$ do until i > npara;
-% $$$ if i > rankHHM;
-% $$$ s[i,i] = 0;
-% $$$ else;
-% $$$ s[i,i] = 1/s[i,i];
-% $$$ invHHMdet = invHHMdet*s[i,i];
-% $$$ endif;
-% $$$ i = i+1;
-% $$$ endo;
-% $$$
-% $$$ invHHM = u*s*u';
-% $$$ sigmult = u*sqrt(s);
-% $$$
-% $$$ "Determinant of minus hessianfit";
-% $$$ invHHMdet;
-% $$$ "sqrt(Diagonal of Inverse hessianfit)";
-% $$$ sqrt(diag(invHHM));
-% $$$
-% $$$ "Post Mode Penalty";
-% $$$ penalt = (rankHHM/2)*ln(2*pi) + 0.5*ln(invHHMdet);
-% $$$ penalt;
-% $$$
-% $$$ /* Initialize Output files
-% $$$ */
-% $$$ omult = lpath $+ "\\" $+ lmodel $+ lprior $+ "mult";
-% $$$ create fhmult=^omult with MULT, npara, 8;
-% $$$ wr = writer(fhmult,sigmult);
-% $$$
-% $$$ closeall fhmult;
-% $$$ end;
-% $$$
-% $$$
-% $$$ %/****************************************************/
-% $$$ %/* PROCEDURES */
-% $$$ %/****************************************************/
-% $$$
-% $$$
-% $$$ %proc (1) = fcn(para);
-% $$$ %local lnpY, lnprio1, lnprio2, obsmean, obsvar;
-% $$$
-% $$$ %{lnpy, obsmean, obsvar} = evallbd( para,mspec,T0,YY);
-% $$$
-% $$$ %/* Evaluate the Prior density
-% $$$
-% $$$ % lnprio1 = priodens( para, pmean, pstdd, pshape);
-% $$$ % lnprio2 = priomuphi( para );
-% $$$
-% $$$ %retp(real(lnpY+lnprio1+lnprio2));
-% $$$ %endp;
-% $$$
-% $$$ /***************************************************************************
-% $$$ */
-% $$$
-% $$$ proc (1) = priomuphi(para);
-% $$$ local muphi, lnprio;
-% $$$ muphi = para[7:8];
-% $$$ if mspec > 1;
-% $$$ lnprio = -ln(2*pi) - 0.5*ln(det(muphi_v0))
-% $$$ - 0.5*(muphi - muphi_m0)'*inv(muphi_v0)*(muphi - muphi_m0);
-% $$$ else;
-% $$$ lnprio = 0;
-% $$$ endif;
-% $$$ retp(lnprio);
-% $$$ endp;
-% $$$
-% $$$
-% $$$
diff --git a/matlab/make_ex_.m b/matlab/make_ex_.m
index 2122d24e0f77652e77b5cf0a0bb8a5cbfc4bf331..c20878acdfded0d85c987e64ac3a4203df6ada0d 100644
--- a/matlab/make_ex_.m
+++ b/matlab/make_ex_.m
@@ -8,8 +8,8 @@ function make_ex_
if isempty(oo_.exo_steady_state)
oo_.exo_steady_state = zeros(M_.exo_nbr,1);
end
- if M_.exo_det_nbr > 1 & isempty(oo_.exo_det_steadystate)
- oo_.exo_det_steadystate = zeros(M_.exo_det_nbr,1);
+ if M_.exo_det_nbr > 1 & isempty(oo_.exo_det_steady_state)
+ oo_.exo_det_steady_state = zeros(M_.exo_det_nbr,1);
end
if isempty(oo_.exo_simul)
if isempty(ex0_)
@@ -36,24 +36,24 @@ function make_ex_
if M_.exo_det_nbr > 0
if isempty(oo_.exo_det_simul)
if isempty(ex_det0_)
- oo_.exo_det_simul = [ones(ykmin_+options_.periods+ykmax_,1)*M_.exo_det_steadystate'];
+ oo_.exo_det_simul = [ones(M_.maximum_lag+options_.periods+M_.maximum_lead,1)*oo_.exo_det_steady_state'];
else
- oo_.exo_det_simul = [ones(ykmin_,1)*ex_det0_';ones(options_.periods+ykmax_,1)*M_.exo_det_steadystate'];
+ oo_.exo_det_simul = [ones(M_.maximum_lag,1)*ex_det0_';ones(options_.periods+M_.maximum_lead,1)*oo_.exo_det_steady_state'];
end
- elseif size(oo_.exo_det_simul,2) < length(M_.exo_det_steadystate)
- k = size(oo_.exo_det_simul,2)+1:length(M_.exo_det_steadystate);
+ elseif size(oo_.exo_det_simul,2) < length(oo_.exo_det_steady_state)
+ k = size(oo_.exo_det_simul,2)+1:length(oo_.exo_det_steady_state);
if isempty(ex_det0_)
- oo_.exo_det_simul = [oo_.exo_det_simul ones(ykmin_+size(oo_.exo_det_simul,1)+ykmax_,1)*M_.exo_det_steadystate(k)'];
+ oo_.exo_det_simul = [oo_.exo_det_simul ones(M_.maximum_lag+size(oo_.exo_det_simul,1)+M_.maximum_lead,1)*oo_.exo_det_steady_state(k)'];
else
- oo_.exo_det_simul = [oo_.exo_det_simul [ones(ykmin_,1)*ex_det0_(k)'; ones(size(oo_.exo_det_simul,1)-ykmin_+ykmax_, ...
- 1)*M_.exo_det_steadystate(k)']];
+ oo_.exo_det_simul = [oo_.exo_det_simul [ones(M_.maximum_lag,1)*ex_det0_(k)'; ones(size(oo_.exo_det_simul,1)-M_.maximum_lag+M_.maximum_lead, ...
+ 1)*oo_.exo_det_steady_state(k)']];
end
- elseif size(oo_.exo_det_simul,1) < ykmin_+ykmax_+options_.periods
+ elseif size(oo_.exo_det_simul,1) < M_.maximum_lag+M_.maximum_lead+options_.periods
if isempty(ex_det0_)
- oo_.exo_det_simul = [oo_.exo_det_simul; ones(ykmin_+options_.periods+ykmax_-size(oo_.exo_det_simul,1),1)*M_.exo_det_steadystate'];
+ oo_.exo_det_simul = [oo_.exo_det_simul; ones(M_.maximum_lag+options_.periods+M_.maximum_lead-size(oo_.exo_det_simul,1),1)*oo_.exo_det_steady_state'];
else
- oo_.exo_det_simul = [ones(ykmin_,1)*ex_det0_'; oo_.exo_det_simul; ones(options_.periods+ykmax_-size(oo_.exo_det_simul, ...
- 1),1)*M_.exo_det_steadystate'];
+ oo_.exo_det_simul = [ones(M_.maximum_lag,1)*ex_det0_'; oo_.exo_det_simul; ones(options_.periods+M_.maximum_lead-size(oo_.exo_det_simul, ...
+ 1),1)*oo_.exo_det_steady_state'];
end
end
end
diff --git a/matlab/mj_optmumlik.m b/matlab/mj_optmumlik.m
deleted file mode 100644
index f7f4f8dfb54ecd0bdb49de66dfc6b0257e5c1d73..0000000000000000000000000000000000000000
--- a/matlab/mj_optmumlik.m
+++ /dev/null
@@ -1,226 +0,0 @@
-% Program calculating the posterior density
-% 1. define xparam
-% 2. call model setup & reduction program
-% 3. prepare state space variables and kalman-filter setup
-% 4. evaluate likelihood with kalman filter
-% 5. evaluate prior
-%------------------------------------------------------------------------------
-%------------------------------------------------------------------------------
-function [fval,cost_flag,atT,innov,ys,trend_coeff] = ...
- mj_optmumlik(xparam1,gend,rawdata,algo);
-
-% algo = 1: computes filter + likelihood
-% alog = 2: computes filter + likelihood + smoother
-
-global M_ oo_ estim_params_ options_
-global bayestopt_ dr1_test_ trend_coeff_ xparam_test
-
-xparam_test = xparam1;
-cost_flag = 1;
-if options_.mode_compute ~= 1 && any(xparam1 < bayestopt_.lb)
- k = find(xparam1 < bayestopt_.lb);
- fval = bayestopt_.penalty+sum(bayestopt_.lb(k)-xparam1(k));
- cost_flag = 0;
- return;
-end
-if options_.mode_compute ~= 1 && any(xparam1 > bayestopt_.ub)
- k = find(xparam1 > bayestopt_.ub);
- fval = bayestopt_.penalty+sum(xparam1(k)-bayestopt_.ub(k));
- cost_flag = 0;
- return;
-end
-
-nobs = size(options_.varobs,1);
-
-q = M_.Sigma_e;
-for i=1:estim_params_.nvx
- k =estim_params_.var_exo(i,1);
- q(k,k) = xparam1(i)*xparam1(i);
-end
-
-offset = estim_params_.nvx;
-h = zeros(nobs,nobs);
-for i=1:estim_params_.nvn
- k =estim_params_.var_endo(i,1);
- h(k,k) = xparam1(i+offset)*xparam1(i+offset);
-end
-
-offset = offset+estim_params_.nvn;
-for i=1:estim_params_.ncx
- k1 =estim_params_.corrx(i,1);
- k2 =estim_params_.corrx(i,2);
- q(k1,k2) = xparam1(i+offset)*sqrt(q(k1,k1)*q(k2,k2));
- q(k2,k1) = q(k1,k2);
-end
-
-offset = offset+estim_params_.ncx;
-for i=1:estim_params_.ncn
- k1 =estim_params_.corrn(i,1);
- k2 =estim_params_.corrn(i,2);
- h(k1,k2) = xparam1(i+offset)*sqrt(h(k1,k1)*h(k2,k2));
- h(k2,k1) = h(k1,k2);
-end
-
-offset = offset+estim_params_.ncn;
-for i=1:estim_params_.np
- %this dosn't work with struct field M_.param
- %assignin('base',deblank(estim_params_.param_names(i,:)),xparam1(i+offset));
- tmpvalue = xparam1(i+offset);
- eval('caller',[ deblank(estim_params_.param_names(i,:)) ' = tmpvalue;']);
-end
-
-%------------------------------------------------------------------------------
-% 2. call model setup & reduction program
-%------------------------------------------------------------------------------
-
-[A,B,ys] = dynare_resolve;
-
-if dr1_test_(1) == 1
- fval = bayestopt_.penalty*exp(dr1_test_(2));
- cost_flag = 0;
- return;
-elseif dr1_test_(1) == 2;
- fval = bayestopt_.penalty*exp(dr1_test_(2));
- cost_flag = 0;
- return;
-elseif dr1_test_(1) == 3;
- fval = bayestopt_.penalty*exp(dr1_test_(2));
- cost_flag = 0;
- return;
-end
-
-if options_.loglinear == 1
- ys1 = log(ys(bayestopt_.mfys));
-else
- ys1 = ys(bayestopt_.mfys);
-end
-
-aa = A;
-
-np = size(A,1);
-mf = eye(np);
-mf = bayestopt_.mf;
-
-% Set initial values @
-
-
-at = zeros(np,gend+1);
-gconst = log(2*pi);
-lik = zeros(gend,1);
-
-if options_.lik_init == 1
- p0 = lyapunov_symm(aa,B*q*B');
-elseif options_.lik_init == 2
- p0=eye(np)*10.0;
-end
-pt = p0;
-BqB = B*q*B';
-
-trend_coeff = zeros(nobs,1);
-if bayestopt_.with_trend == 1
- nx1 = estim_params_.nvx+estim_params_.nvn+estim_params_.ncx+ ...
- estim_params_.ncn;
- for i=1:nobs
- trend_coeff(i) = eval(bayestopt_.trend_coeff{i});
- end
-end
-
-not_steady = 1;
-ldetf_old = NaN;
-warning_state = warning;
-warning off;
-if algo == 1
- for t = 1:gend
- if not_steady
- ptt1 = aa*pt*aa'+BqB;
- f = ptt1(mf,mf)+h;
- ldetf = log(det(f));
- finv = inv(f);
- if any(isinf(finv)) | ~isreal(ldetf)
- disp('singularity in Kalman filter');
- fval = bayestopt_.penalty;
- warning(warning_state);
- cost_flag = 0;
- dr1_test_(1) = 4;
- return
- end
- pt = ptt1-ptt1(:,mf)*finv*ptt1(mf,:);
- if abs(ldetf-ldetf_old) < 1e-12
- not_steady = 0;
- end
- ldetf_old = ldetf;
- end
- att1 = aa*at(:,t);
- v = rawdata(t,:)'-att1(mf,:)-ys1;
- if bayestopt_.with_trend == 1
- v = v - trend_coeff*t;
- end
- at(:,t+1) = att1+ptt1(:,mf)*finv*v;
- if t > options_.presample
- lik(t,1) = ldetf+v'*finv*v;
- end
- end
-elseif algo == 2
- PPt = zeros(np^2,gend);
- for t = 1:gend
- if not_steady
- ptt1 = aa*pt*aa'+BqB;
- f = ptt1(mf,mf)+h;
- ldetf = log(det(f));
- finv = inv(f);
- if any(isinf(finv)) | ~isreal(ldetf)
-% disp('singularity in Kalman filter');
- fval = bayestopt_.penalty;
- warning(warning_state);
- cost_flag = 0;
- dr1_test_(1) = 4;
- return
- end
- pt = ptt1-ptt1(:,mf)*finv*ptt1(mf,:);
- if abs(ldetf-ldetf_old) < 1e-12
- not_steady = 0;
- end
- ldetf_old = ldetf;
- end
- att1 = aa*at(:,t);
- v = rawdata(t,:)' - att1(mf,:) - ys1;
- if bayestopt_.with_trend == 1
- v = v - trend_coeff*t;
- end
- at(:,t+1) = att1+ptt1(:,mf)*finv*v;
- PPt(:,t) = pt(:);
- if t > options_.presample
- lik(t,1) = ldetf+v'*finv*v;
- end
- end
- atT =zeros(np,gend+1);
- atT(:,gend+1) = at(:,gend+1);
- innov = zeros(M_.exo_nbr,gend);
- for t = gend:-1:1
- pt = reshape(PPt(:,t),np,np);
- ptt1 = aa*pt*aa'+BqB;
- Pstar = pt*aa'*pinv(ptt1);
- atT(:,t) = at(:,t)+Pstar*(atT(:,t+1)-aa*at(:,t));
- shocks1 = atT(:,t+1)-aa*atT(:,t);
- innov(:,t) = B\shocks1;
- end
-end
-
-warning(warning_state);
-
-likelihood = 0.5*sum(nobs*gconst+lik(options_.presample+1:end));
-
-if imag(likelihood) ~= 0
-
- likelihood = 10000000;
-end
-
-% ------------------------------------------------------------------------------
-% PRIOR SPECIFICATION
-% ------------------------------------------------------------------------------
-
-lnprior = priordens(xparam1, bayestopt_.pshape, bayestopt_.p1, bayestopt_.p2, bayestopt_.p3, bayestopt_.p4 );
-
-fval = (likelihood-lnprior);
-
-% 11/18/03 MJ changed input parameters for priordens()
\ No newline at end of file
diff --git a/matlab/resol.m b/matlab/resol.m
index c54df7bf024dfdd293f8e40f62f98f0eb23daf9e..10dc3e0b585486f00566d6acd6f278ee94f7158b 100644
--- a/matlab/resol.m
+++ b/matlab/resol.m
@@ -11,6 +11,7 @@ global it_
options_ = set_default_option(options_,'olr',0);
+options_ = set_default_option(options_,'jacobian_flag',1);
info = 0;
it_ = M_.maximum_lag + 1 ;
@@ -24,17 +25,18 @@ tempex = oo_.exo_simul;
oo_.exo_simul = repmat(oo_.exo_steady_state',M_.maximum_lag+M_.maximum_lead+1,1);
if M_.exo_det_nbr > 0
tempexdet = oo_.exo_det_simul;
- oo_.exo_det_simul = ones(M_.maximum_lag+1,1)*oo_.exo_steady_statedet_';
+ oo_.exo_det_simul = repmat(oo_.exo_det_steady_state',M_.maximum_lag+M_.maximum_lead+1,1);
end
dr.ys = ys;
fh = str2func([M_.fname '_static']);
if options_.linear == 0
- if max(abs(feval(fh,dr.ys,oo_.exo_steady_state))) > options_.dynatol & options_.olr == 0
+ if max(abs(feval(fh,dr.ys,[oo_.exo_steady_state; oo_.exo_det_steady_state]))) > options_.dynatol & options_.olr == 0
if exist([M_.fname '_steadystate'])
- [dr.ys,check1] = feval([M_.fname '_steadystate'],dr.ys,oo_.exo_steady_state);
+ [dr.ys,check1] = feval([M_.fname '_steadystate'],dr.ys,...
+ [oo_.exo_steady_state; oo_.exo_det_steady_state]);
else
- %[dr.ys,check1] = dynare_solve(fh,dr.ys,oo_.exo_steady_state);
- [dr.ys,check1] = dynare_solve(fh,dr.ys,1,oo_.exo_steady_state);
+ [dr.ys,check1] = dynare_solve(fh,dr.ys,options_.jacobian_flag,...
+ [oo_.exo_steady_state; oo_.exo_det_steady_state]);
end
if check1
info(1) = 20;
diff --git a/matlab/steady.m b/matlab/steady.m
index e18b7989678d4a89f08bd7c6e1e0b5dda0073eef..5bb063f9c8367694c24a1c08fe375867ce24e680 100644
--- a/matlab/steady.m
+++ b/matlab/steady.m
@@ -4,6 +4,8 @@ function steady(linear)
global M_ oo_ options_ ys0_
+ options_ = set_default_option(options_,'jacobian_flag',1);
+
steady_;
disp(' ')
diff --git a/matlab/steady_.m b/matlab/steady_.m
index 0ad3e296fde79a852c7898202b21bdcbc6d33794..3b5326226243ed7a41b64dfbb83339a8c4481b3f 100644
--- a/matlab/steady_.m
+++ b/matlab/steady_.m
@@ -2,23 +2,19 @@
%
function steady_()
- global M_ oo_ it_
-
-
- x = oo_.steady_state ;
- xlen = M_.maximum_lag + M_.maximum_lead + 1 ;
- nn = size(M_.lead_lag_incidence,2) ;
- it_ = M_.maximum_lag+1 ;
- x = repmat(oo_.exo_steady_state',xlen,1);
-
- if M_.exo_det_nbr > 0
- x = [x, repmat(oo_.exo_det_steady_state',M_.maximum_lag+1,1)] ;
- end
+ global M_ oo_ it_ options_
if exist([M_.fname '_steadystate'])
- [oo_.steady_state,check] = feval([M_.fname '_steadystate'],oo_.steady_state,x);
+ [oo_.steady_state,check] = feval([M_.fname '_steadystate'],...
+ oo_.steady_state,...
+ [oo_.exo_steady_state; ...
+ oo_.exo_det_steady_state]);
else
- [oo_.steady_state,check] = dynare_solve([M_.fname '_static'],oo_.steady_state,1,x);
+ [oo_.steady_state,check] = dynare_solve([M_.fname '_static'],...
+ oo_.steady_state,...
+ options_.jacobian_flag, ...
+ [oo_.exo_steady_state; ...
+ oo_.exo_det_steady_state]);
end
if check ~= 0