stepan
/
dynare
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

- Corrects the first order approximation of block-decomposed models 

- The block-decomposed models can now be estimated
time-shift
Ferhat Mihoubi 2011-06-18 17:43:45 +02:00
parent fe1b241186
commit 3459b08c53
8 changed files with 432 additions and 185 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
matlab/block_mfs_steadystate.m
View File

@ -21,9 +21,7 @@ function [r, g1] = block_mfs_steadystate(y, b, y_all)
global M_ oo_
indx = M_.blocksMFS{b};
y_all(indx) = y;
y_all(M_.blocksMFS{b}) = y;
x = [oo_.exo_steady_state; oo_.exo_det_steady_state];
eval(['[r,g1] = ' M_.fname '_static(b, y_all, x, M_.params);']);

2
matlab/check.m
View File

@ -55,7 +55,7 @@ oo_.exo_simul = tempex;
eigenvalues_ = dr.eigval;
if (options_.block)
nyf = dr.nfwrd+dr.nboth;
nyf = dr.nyf;
else
nyf = nnz(dr.kstate(:,2)>M_.maximum_endo_lag+1);
end;

12
matlab/disp_dr.m
View File

@ -109,7 +109,11 @@ for k=1:nx
end;
str = sprintf('%-20s',str1);
for i=1:nvar
x = dr.ghx(ivar(i),k);
if options_.block
x = dr.ghx(i,k);
else
x = dr.ghx(ivar(i),k);
end;
if abs(x) > 1e-6
flag = 1;
str = [str sprintf('%16.6f',x)];
@ -128,7 +132,11 @@ for k=1:nu
flag = 0;
str = sprintf('%-20s',M_.exo_names(k,:));
for i=1:nvar
x = dr.ghu(ivar(i),k);
if options_.block
x = dr.ghu(i,k);
else
x = dr.ghu(ivar(i),k);
end;
if abs(x) > 1e-6
flag = 1;
str = [str sprintf('%16.6f',x)];

534
matlab/dr_block.m
View File

@ -27,7 +27,7 @@ function [dr,info,M_,options_,oo_] = dr_block(dr,task,M_,options_,oo_)
%
% ALGORITHM
% first order block relaxation method applied to the model
% E[A Yt-1 + B Yt + C Yt-1 + ut] = 0
% E[A Yt-1 + B Yt + C Yt+1 + ut] = 0
%
% SPECIAL REQUIREMENTS
% none.
@ -52,7 +52,10 @@ function [dr,info,M_,options_,oo_] = dr_block(dr,task,M_,options_,oo_)
info = 0;
verbose = 0;
%it_ = M_.maximum_lag + 1;
if options_.order > 1
error('2nd and 3rd order approximation not implemented with block option')
end
z = repmat(dr.ys,1,M_.maximum_lead + M_.maximum_lag + 1);
if (isfield(M_,'block_structure'))
data = M_.block_structure.block;
@ -62,11 +65,9 @@ else
Size = 1;
end;
if (options_.bytecode)
[chck, zz, data]= bytecode('dynamic','evaluate',z,[oo_.exo_simul ...
oo_.exo_det_simul], M_.params, dr.ys, 1, data);
[chck, zz, data]= bytecode('dynamic','evaluate',z,[oo_.exo_simul oo_.exo_det_simul], M_.params, dr.ys, 1, data);
else
[r, data] = feval([M_.fname '_dynamic'], z', [oo_.exo_simul ...
oo_.exo_det_simul], M_.params, dr.ys, 2, data);
[r, data] = feval([M_.fname '_dynamic'], z', [oo_.exo_simul oo_.exo_det_simul], M_.params, dr.ys, M_.maximum_lag+1, data);
chck = 0;
end;
mexErrCheck('bytecode', chck);
@ -77,14 +78,26 @@ dr.nfwrd = 0;
dr.npred = 0;
dr.nboth = 0;
dr.nd = 0;
dr.state_var = [];
dr.exo_var = [];
dr.ghx = [];
dr.ghu = [];
%Determine the global list of state variables:
dr.state_var = M_.state_var;
M_.block_structure.state_var = dr.state_var;
n_sv = size(dr.state_var, 2);
dr.ghx = zeros(M_.endo_nbr, length(dr.state_var));
dr.exo_var = 1:M_.exo_nbr;
dr.ghu = zeros(M_.endo_nbr, M_.exo_nbr);
dr.nstatic = M_.nstatic;
dr.nfwrd = M_.nfwrd;
dr.npred = M_.npred;
dr.nboth = M_.nboth;
dr.nyf = 0;
for i = 1:Size;
ghx = [];
indexi_0 = 0;
if (verbose)
disp('======================================================================');
disp(['Block ' int2str(i)]);
disp('-----------');
data(i)
@ -93,15 +106,13 @@ for i = 1:Size;
n_fwrd = data(i).n_forward;
n_both = data(i).n_mixed;
n_static = data(i).n_static;
dr.nstatic = dr.nstatic + n_static;
dr.nfwrd = dr.nfwrd + n_fwrd;
dr.npred = dr.npred + n_pred;
dr.nboth = dr.nboth + n_both;
dr.nyf = dr.nyf + n_fwrd + n_both;
nd = n_pred + n_fwrd + 2*n_both;
dr.nd = dr.nd + nd;
n_dynamic = n_pred + n_fwrd + n_both;
exo_nbr = M_.block_structure.block(i).exo_nbr;
exo_det_nbr = M_.block_structure.block(i).exo_det_nbr;
other_endo_nbr = M_.block_structure.block(i).other_endo_nbr;
jacob = full(data(i).g1);
lead_lag_incidence = data(i).lead_lag_incidence;
endo = data(i).variable;
@ -116,48 +127,120 @@ for i = 1:Size;
maximum_lead = data(i).maximum_endo_lead;
n = n_dynamic + n_static;
switch M_.block_structure.block(i).Simulation_Type
block_type = M_.block_structure.block(i).Simulation_Type;
if task ~= 1
if block_type == 2 || block_type == 4 || block_type == 6
block_type = 8;
end;
end;
if maximum_lag > 0 && n_pred > 0 && block_type ~= 1
indexi_0 = min(lead_lag_incidence(2,:));
end;
switch block_type
case 1
%% ------------------------------------------------------------------
%Evaluate Forward
if maximum_lag > 0 && n_pred > 0
indx_r = find(M_.block_structure.block(i).lead_lag_incidence(1,:));
indx_c = M_.block_structure.block(i).lead_lag_incidence(1,indx_r);
data(i).eigval = diag(jacob(indx_r, indx_c));
data(i).rank = sum(abs(data(i).eigval) > 0);
data(i).rank = 0;
else
data(i).eigval = [];
data(i).rank = 0;
end
dr.eigval = [dr.eigval ; data(i).eigval];
dr.rank = dr.rank + data(i).rank;
%First order approximation
if task ~= 1
if (maximum_lag > 0)
indexi_0 = min(lead_lag_incidence(2,:));
indx_r = find(M_.block_structure.block(i).lead_lag_incidence(1,:));
indx_c = M_.block_structure.block(i).lead_lag_incidence(1,indx_r);
ghx = jacob(indx_r, indx_c);
[tmp1, tmp2, indx_c] = find(M_.block_structure.block(i).lead_lag_incidence(2,:));
B = jacob(:,indx_c);
if (maximum_lag > 0 && n_pred > 0)
[indx_r, tmp1, indx_r_v] = find(M_.block_structure.block(i).lead_lag_incidence(1,:));
ghx = - B \ jacob(:,indx_r_v);
end;
ghu = data(i).g1_x;
if other_endo_nbr
fx = data(i).g1_o;
% retrieves the derivatives with respect to endogenous
% variable belonging to previous blocks
fx_tm1 = zeros(n,other_endo_nbr);
fx_t = zeros(n,other_endo_nbr);
fx_tp1 = zeros(n,other_endo_nbr);
% stores in fx_tm1 the lagged values of fx
[r, c, lag] = find(data(i).lead_lag_incidence_other(1,:));
fx_tm1(:,c) = fx(:,lag);
% stores in fx the current values of fx
[r, c, curr] = find(data(i).lead_lag_incidence_other(2,:));
fx_t(:,c) = fx(:,curr);
% stores in fx_tm1 the leaded values of fx
[r, c, lead] = find(data(i).lead_lag_incidence_other(3,:));
fx_tp1(:,c) = fx(:,lead);
l_x = dr.ghx(data(i).other_endogenous,:);
l_x_sv = dr.ghx(dr.state_var, 1:n_sv);
selector_tm1 = M_.block_structure.block(i).tm1;
ghx_other = - B \ (fx_t * l_x + (fx_tp1 * l_x * l_x_sv) + fx_tm1 * selector_tm1);
dr.ghx(endo, :) = dr.ghx(endo, :) + ghx_other;
end;
if exo_nbr
fu = data(i).g1_x;
exo = dr.exo_var;
if other_endo_nbr > 0
l_u_sv = dr.ghu(dr.state_var,:);
l_x = dr.ghx(data(i).other_endogenous,:);
l_u = dr.ghu(data(i).other_endogenous,:);
fu_complet = zeros(n, M_.exo_nbr);
fu_complet(:,data(i).exogenous) = fu;
ghu = - B \ (fu_complet + fx_tp1 * l_x * l_u_sv + (fx_t) * l_u );
else
fu_complet = zeros(n, M_.exo_nbr);
fu_complet(:,data(i).exogenous) = fu;
ghu = - B \ fu_complet;
end;
else
exo = dr.exo_var;
if other_endo_nbr > 0
l_u_sv = dr.ghu(dr.state_var,:);
l_x = dr.ghx(data(i).other_endogenous,:);
l_u = dr.ghu(data(i).other_endogenous,:);
ghu = -B \ (fx_tp1 * l_x * l_u_sv + (fx_t) * l_u );
else
ghu = [];
end
end
end
case 2
%% ------------------------------------------------------------------
%Evaluate Backward
if maximum_lead > 0 && n_fwrd > 0
indx_r = find(M_.block_structure.block(i).lead_lag_incidence(2,:));
indx_c = M_.block_structure.block(i).lead_lag_incidence(2,indx_r);
data(i).eigval = 1./ diag(jacob(indx_r, indx_c));
data(i).eigval = 1 ./ diag(jacob(indx_r, indx_c));
data(i).rank = sum(abs(data(i).eigval) > 0);
else
data(i).eigval = [];
data(i).rank = 0;
end
dr.rank = dr.rank + data(i).rank;
dr.eigval = [dr.eigval ; data(i).eigval];
dr.rank = dr.rank + data(i).rank;
%First order approximation
if task ~= 1
if (maximum_lag > 0)
indx_r = find(M_.block_structure.block(i).lead_lag_incidence(3,:));
indx_c = M_.block_structure.block(i).lead_lag_incidence(3,indx_r);
ghx = - inv(jacob(indx_r, indx_c));
end;
ghu = - inv(jacob(indx_r, indx_c)) * data(i).g1_x;
end
case 3
%Solve Forward simple
%% ------------------------------------------------------------------
%Solve Forward single equation
if maximum_lag > 0 && n_pred > 0
data(i).eigval = - jacob(1 , 1 : n_pred) / jacob(1 , n_pred + n_static + 1 : n_pred + n_static + n_pred + n_both);
data(i).rank = sum(abs(data(i).eigval) > 0);
data(i).rank = 0;
else
data(i).eigval = [];
data(i).rank = 0;
@ -166,13 +249,63 @@ for i = 1:Size;
%First order approximation
if task ~= 1
if (maximum_lag > 0)
indexi_0 = min(lead_lag_incidence(2,:));
ghx = - jacob(1 , 1 : n_pred) / jacob(1 , n_pred + n_static + 1 : n_pred + n_static + n_pred + n_both);
ghx = - jacob(1 , 1 : n_pred) / jacob(1 , n_pred + n_static + 1 : n_pred + n_static + n_pred + n_both);
else
ghx = 0;
end;
ghu = data(i).g1_x;
if other_endo_nbr
fx = data(i).g1_o;
% retrieves the derivatives with respect to endogenous
% variable belonging to previous blocks
fx_tm1 = zeros(n,other_endo_nbr);
fx_t = zeros(n,other_endo_nbr);
fx_tp1 = zeros(n,other_endo_nbr);
% stores in fx_tm1 the lagged values of fx
[r, c, lag] = find(data(i).lead_lag_incidence_other(1,:));
fx_tm1(:,c) = fx(:,lag);
% stores in fx the current values of fx
[r, c, curr] = find(data(i).lead_lag_incidence_other(2,:));
fx_t(:,c) = fx(:,curr);
% stores in fx_tm1 the leaded values of fx
[r, c, lead] = find(data(i).lead_lag_incidence_other(3,:));
fx_tp1(:,c) = fx(:,lead);
l_x = dr.ghx(data(i).other_endogenous,:);
l_x_sv = dr.ghx(dr.state_var, 1:n_sv);
selector_tm1 = M_.block_structure.block(i).tm1;
ghx_other = - (fx_t * l_x + (fx_tp1 * l_x * l_x_sv) + fx_tm1 * selector_tm1) / jacob(1 , n_pred + 1 : n_pred + n_static + n_pred + n_both);
dr.ghx(endo, :) = dr.ghx(endo, :) + ghx_other;
end;
if exo_nbr
fu = data(i).g1_x;
if other_endo_nbr > 0
l_u_sv = dr.ghu(dr.state_var,:);
l_x = dr.ghx(data(i).other_endogenous,:);
l_u = dr.ghu(data(i).other_endogenous,:);
fu_complet = zeros(n, M_.exo_nbr);
fu_complet(:,data(i).exogenous) = fu;
ghu = -(fu_complet + fx_tp1 * l_x * l_u_sv + (fx_t) * l_u ) / jacob(1 , n_pred + 1 : n_pred + n_static + n_pred + n_both);
exo = dr.exo_var;
else
ghu = - fu / jacob(1 , n_pred + 1 : n_pred + n_static + n_pred + n_both);
end;
else
if other_endo_nbr > 0
l_u_sv = dr.ghu(dr.state_var,:);
l_x = dr.ghx(data(i).other_endogenous,:);
l_u = dr.ghu(data(i).other_endogenous,:);
ghu = -(fx_tp1 * l_x * l_u_sv + (fx_t) * l_u ) / jacob(1 , n_pred + 1 : n_pred + n_static + n_pred + n_both);
exo = dr.exo_var;
else
ghu = [];
end
end
end
case 4
%Solve Backward simple
%% ------------------------------------------------------------------
%Solve Backward single equation
if maximum_lead > 0 && n_fwrd > 0
data(i).eigval = - jacob(1 , n_pred + n - n_fwrd + 1 : n_pred + n) / jacob(1 , n_pred + n + 1 : n_pred + n + n_fwrd) ;
data(i).rank = sum(abs(data(i).eigval) > 0);
@ -183,17 +316,74 @@ for i = 1:Size;
dr.rank = dr.rank + data(i).rank;
dr.eigval = [dr.eigval ; data(i).eigval];
case 5
%% ------------------------------------------------------------------
%Solve Forward complete
if maximum_lag > 0 && n_pred > 0
data(i).eigval = eig(- jacob(: , 1 : n_pred) / ...
jacob(: , (n_pred + n_static + 1 : n_pred + n_static + n_pred )));
data(i).rank = sum(abs(data(i).eigval) > 0);
data(i).rank = 0;
else
data(i).eigval = [];
data(i).rank = 0;
end;
dr.eigval = [dr.eigval ; data(i).eigval];
if task ~= 1
if (maximum_lag > 0)
ghx = - jacob(1 , 1 : n_pred) / jacob(1 , n_pred + n_static + 1 : n_pred + n_static + n_pred + n_both);
else
ghx = 0;
end;
if other_endo_nbr
fx = data(i).g1_o;
% retrieves the derivatives with respect to endogenous
% variable belonging to previous blocks
fx_tm1 = zeros(n,other_endo_nbr);
fx_t = zeros(n,other_endo_nbr);
fx_tp1 = zeros(n,other_endo_nbr);
% stores in fx_tm1 the lagged values of fx
[r, c, lag] = find(data(i).lead_lag_incidence_other(1,:));
fx_tm1(:,c) = fx(:,lag);
% stores in fx the current values of fx
[r, c, curr] = find(data(i).lead_lag_incidence_other(2,:));
fx_t(:,c) = fx(:,curr);
% stores in fx_tm1 the leaded values of fx
[r, c, lead] = find(data(i).lead_lag_incidence_other(3,:));
fx_tp1(:,c) = fx(:,lead);
l_x = dr.ghx(data(i).other_endogenous,:);
l_x_sv = dr.ghx(dr.state_var, 1:n_sv);
selector_tm1 = M_.block_structure.block(i).tm1;
ghx_other = - (fx_t * l_x + (fx_tp1 * l_x * l_x_sv) + fx_tm1 * selector_tm1) / jacob(1 , n_pred + 1 : n_pred + n_static + n_pred + n_both);
dr.ghx(endo, :) = dr.ghx(endo, :) + ghx_other;
end;
if exo_nbr
fu = data(i).g1_x;
if other_endo_nbr > 0
l_u_sv = dr.ghu(dr.state_var,:);
l_x = dr.ghx(data(i).other_endogenous,:);
l_u = dr.ghu(data(i).other_endogenous,:);
fu_complet = zeros(n, M_.exo_nbr);
fu_complet(:,data(i).exogenous) = fu;
ghu = -(fu_complet + fx_tp1 * l_x * l_u_sv + (fx_t) * l_u ) / jacob(1 , n_pred + 1 : n_pred + n_static + n_pred + n_both);
exo = dr.exo_var;
else
ghu = - fu / jacob(1 , n_pred + 1 : n_pred + n_static + n_pred + n_both);
end;
else
if other_endo_nbr > 0
l_u_sv = dr.ghu(dr.state_var,:);
l_x = dr.ghx(data(i).other_endogenous,:);
l_u = dr.ghu(data(i).other_endogenous,:);
ghu = -(fx_tp1 * l_x * l_u_sv + (fx_t) * l_u ) / jacob(1 , n_pred + 1 : n_pred + n_static + n_pred + n_both);
exo = dr.exo_var;
else
ghu = [];
end
end
end
case 6
%% ------------------------------------------------------------------
%Solve Backward complete
if maximum_lead > 0 && n_fwrd > 0
data(i).eigval = eig(- jacob(: , n_pred + n - n_fwrd + 1: n_pred + n))/ ...
@ -206,26 +396,25 @@ for i = 1:Size;
dr.rank = dr.rank + data(i).rank;
dr.eigval = [dr.eigval ; data(i).eigval];
case 8
%The lead_lag_incidence contains columns in the following order :
%% ------------------------------------------------------------------
%The lead_lag_incidence contains columns in the following order:
% static variables, backward variable, mixed variables and forward variables
%
%Procedes to a QR decomposition on the jacobian matrix to reduce the problem size
%Proceeds to a QR decomposition on the jacobian matrix in order to reduce the problem size
index_c = lead_lag_incidence(2,:); % Index of all endogenous variables present at time=t
index_s = lead_lag_incidence(2,1:n_static); % Index of all static endogenous variables present at time=t
if n_static > 0
[Q, R] = qr(jacob(:,index_s));
[Q, junk] = qr(jacob(:,index_s));
aa = Q'*jacob;
else
aa = jacob;
end;
indexi_0 = min(lead_lag_incidence(2,:));
index_0m = (n_static+1:n_static+n_pred) + indexi_0 - 1;
index_0p = (n_static+n_pred+1:n) + indexi_0 - 1;
index_m = 1:(n_pred+n_both);
indexi_p = max(lead_lag_incidence(2,:))+1;
index_p = indexi_p:size(jacob, 2);
nyf = n_fwrd + n_both;
A = aa(:,index_m); % Jacobain matrix for lagged endogeneous variables
B = aa(:,index_c); % Jacobian matrix for contemporaneous endogeneous variables
C = aa(:,index_p); % Jacobain matrix for led endogeneous variables
@ -236,6 +425,7 @@ for i = 1:Size;
E = [-aa(row_indx,[index_m index_0p]) ; [zeros(n_both, n_both + n_pred) eye(n_both, n_both + n_fwrd) ] ];
[err, ss, tt, w, sdim, data(i).eigval, info1] = mjdgges(E,D,options_.qz_criterium);
if (verbose)
disp('eigval');
disp(data(i).eigval);
@ -245,7 +435,6 @@ for i = 1:Size;
info(2) = info1;
return
end
%sdim
nba = nd-sdim;
if task == 1
data(i).rank = rank(w(nd-nyf+1:end,nd-nyf+1:end));
@ -268,11 +457,11 @@ for i = 1:Size;
if isfield(options_,'indeterminacy_continuity')
if options_.indeterminacy_msv == 1
[ss,tt,w,q] = qz(e',d');
[ss,tt,w,q] = reorder(ss,tt,w,q);
[ss,tt,w,junk] = reorder(ss,tt,w,q);
ss = ss';
tt = tt';
w = w';
nba = nyf;
%nba = nyf;
end
else
if nba > nyf
@ -286,24 +475,24 @@ for i = 1:Size;
return
end
end
indx_stable_root = 1: (nd - nyf); %=> index of stable roots
indx_explosive_root = n_pred + 1:nd; %=> index of explosive roots
indx_stable_root = 1: (nd - nyf); %=> index of stable roots
indx_explosive_root = n_pred + n_both + 1:nd; %=> index of explosive roots
% derivatives with respect to dynamic state variables
% forward variables
Z = w';
Z11t = Z(indx_stable_root, indx_stable_root)';
Z21 = Z(indx_explosive_root, indx_stable_root);
Z22 = Z(indx_explosive_root, indx_explosive_root);
if ~isfloat(Z21) && (condest(Z21) > 1e9)
% condest() fails on a scalar under Octave
info(1) = 5;
info(2) = condest(Z21);
return;
else
gx = -inv(Z22) * Z21;
%gx = -inv(Z22) * Z21;
gx = - Z22 \ Z21;
end
% predetermined variables
hx = Z11t * inv(tt(indx_stable_root, indx_stable_root)) * ss(indx_stable_root, indx_stable_root) * inv(Z11t);
@ -312,157 +501,178 @@ for i = 1:Size;
ghx = [hx(k1,:); gx(k2(n_both+1:end),:)];
if (verbose)
disp('ghx');
disp(ghx);
end;
%lead variables actually present in the model
j4 = n_static+n_pred+1:n_static+n_pred+n_both+n_fwrd;
j3 = nonzeros(lead_lag_incidence(2,j4)) - n_static - 2 * n_pred - n_both;
j4 = find(lead_lag_incidence(2,j4));
if (verbose)
disp('j3');
disp(j3);
disp('j4');
disp(j4);
j4 = n_static+n_pred+1:n_static+n_pred+n_both+n_fwrd; % Index on the forward and both variables
j3 = nonzeros(lead_lag_incidence(2,j4)) - n_static - 2 * n_pred - n_both; % Index on the non-zeros forward and both variables
j4 = find(lead_lag_incidence(2,j4));
if n_static > 0
B_static = B(:,1:n_static); % submatrix containing the derivatives w.r. to static variables
else
B_static = [];
end;
%static variables, backward variable, mixed variables and forward variables
B_pred = B(:,n_static+1:n_static+n_pred+n_both);
B_fyd = B(:,n_static+n_pred+n_both+1:end);
% static variables
if n_static > 0
temp = - C(1:n_static,j3)*gx(j4,:)*hx;
j5 = index_m;
b = aa(:,index_c);
b10 = b(1:n_static, 1:n_static);
b11 = b(1:n_static, n_static+1:n);
temp(:,j5) = temp(:,j5)-A(1:n_static,:);
temp = b10\(temp-b11*ghx);
ghx = [temp; ghx];
temp = [];
end;
if other_endo_nbr
if n_static > 0
fx = Q' * data(i).g1_o;
else
fx = data(i).g1_o;
end;
% retrieves the derivatives with respect to endogenous
% variable belonging to previous blocks
fx_tm1 = zeros(n,other_endo_nbr);
fx_t = zeros(n,other_endo_nbr);
fx_tp1 = zeros(n,other_endo_nbr);
% stores in fx_tm1 the lagged values of fx
[r, c, lag] = find(data(i).lead_lag_incidence_other(1,:));
fx_tm1(:,c) = fx(:,lag);
% stores in fx the current values of fx
[r, c, curr] = find(data(i).lead_lag_incidence_other(2,:));
fx_t(:,c) = fx(:,curr);
% stores in fx_tp1 the leaded values of fx
[r, c, lead] = find(data(i).lead_lag_incidence_other(3,:));
fx_tp1(:,c) = fx(:,lead);
l_x = dr.ghx(data(i).other_endogenous,:);
l_x_sv = dr.ghx(dr.state_var, :);
selector_tm1 = M_.block_structure.block(i).tm1;
A_ = real([B_static C(:,j3)*gx+B_pred B_fyd]); % The state_variable of the block are located at [B_pred B_both]
B_ = [zeros(size(B_static)) zeros(n,n_pred) C(:,j3) ];
C_ = l_x_sv;
D_ = (fx_t * l_x + fx_tp1 * l_x * l_x_sv + fx_tm1 * selector_tm1 );
% Solve the Sylvester equation:
% A_ * gx + B_ * gx * C_ + D_ = 0
%vghx_other = - inv(kron(eye(size(D_,2)), A_) + kron(C_', B_)) * vec(D_);
%ghx_other = reshape(vghx_other, size(D_,1), size(D_,2));
ghx_other = sylvester3(A_, B_, C_, -D_);
if options_.aim_solver ~= 1 && options_.use_qzdiv
% Necessary when using Sims' routines for QZ
ghx_other = real(ghx_other);
end
dr.ghx(endo, :) = dr.ghx(endo, :) + ghx_other;
end;
% derivatives with respect to exogenous variables
if exo_nbr
if n_static > 0
fu = Q' * data(i).g1_x;
else
fu = data(i).g1_x;
end;
B_static = [];
if n_static > 0
B_static = B(:,1:n_static); % submatrix containing the derivatives w.r. to static variables
end
B_pred = B(:,n_static+1:n_static+n_pred);
B_fyd = B(:,n_static+n_pred+1:end);
ghu = -[B_static C(:,j3)*gx(j4,1:n_pred)+B_pred B_fyd]\fu;
if (verbose)
disp('ghu');
disp(ghu);
if other_endo_nbr > 0
l_u_sv = dr.ghu(dr.state_var,:);
l_x = dr.ghx(data(i).other_endogenous,:);
l_u = dr.ghu(data(i).other_endogenous,:);
fu_complet = zeros(n, M_.exo_nbr);
fu_complet(:,data(i).exogenous) = fu;
% Solve the equation in ghu:
% A_ * ghu + (fu_complet + fx_tp1 * l_x * l_u_sv + (fx_t + B_ * ghx_other) * l_u ) = 0
ghu = -A_\ (fu_complet + fx_tp1 * l_x * l_u_sv + fx_t * l_u + B_ * ghx_other * l_u_sv );
exo = dr.exo_var;
else
ghu = - A_ / fu;
end;
else
ghu = [];
if other_endo_nbr > 0
l_u_sv = dr.ghu(dr.state_var,:);
l_x = dr.ghx(data(i).other_endogenous,:);
l_u = dr.ghu(data(i).other_endogenous,:);
% Solve the equation in ghu:
% A_ * ghu + (fx_tp1 * l_x * l_u_sv + (fx_t + B_ * ghx_other) * l_u ) = 0
ghu = -real(A_)\ (fx_tp1 * l_x * l_u_sv + (fx_t * l_u + B_ * ghx_other * l_u_sv) );
exo = dr.exo_var;
else
ghu = [];
end;
end
% static variables
if n_static > 0
temp = - C(1:n_static,j3)*gx(j4,:)*hx;
if (verbose)
disp('temp');
disp(temp);
end;
j5 = index_m;
if (verbose)
disp('j5');
disp(j5);
end;
b = aa(:,index_c);
b10 = b(1:n_static, 1:n_static);
b11 = b(1:n_static, n_static+1:n);
if (verbose)
disp('b10');
disp(b10);
disp('b11');
disp(b11);
end;
temp(:,j5) = temp(:,j5)-A(1:n_static,:);
if (verbose)
disp('temp');
disp(temp);
end;
disp(temp-b11*ghx);
temp = b10\(temp-b11*ghx);
if (verbose)
disp('temp');
disp(temp);
end;
ghx = [temp; ghx];
temp = [];
if (verbose)
disp('ghx');
disp(ghx);
end;
end
if options_.loglinear == 1
k = find(dr.kstate(:,2) <= M_.maximum_endo_lag+1);
klag = dr.kstate(k,[1 2]);
k1 = dr.order_var;
ghx = repmat(1./dr.ys(k1),1,size(ghx,2)).*ghx.* ...
repmat(dr.ys(k1(klag(:,1)))',size(ghx,1),1);
ghu = repmat(1./dr.ys(k1),1,size(ghu,2)).*ghu;
error('log linear option is for the moment not supported in first order approximation for a block decomposed mode');
% k = find(dr.kstate(:,2) <= M_.maximum_endo_lag+1);
% klag = dr.kstate(k,[1 2]);
% k1 = dr.order_var;
%
% ghx = repmat(1./dr.ys(k1),1,size(ghx,2)).*ghx.* ...
% repmat(dr.ys(k1(klag(:,1)))',size(ghx,1),1);
% ghu = repmat(1./dr.ys(k1),1,size(ghu,2)).*ghu;
end
if options_.aim_solver ~= 1 && options_.use_qzdiv
% Necessary when using Sims' routines for QZ
gx = real(gx);
hx = real(hx);
ghx = real(ghx);
ghu = real(ghu);
end
ghx
%exogenous deterministic variables
if exo_det_nbr > 0
f1 = sparse(jacobia_(:,nonzeros(M_.lead_lag_incidence(M_.maximum_endo_lag+2:end,order_var))));
f0 = sparse(jacobia_(:,nonzeros(M_.lead_lag_incidence(M_.maximum_endo_lag+1,order_var))));
fudet = data(i).g1_xd;
M1 = inv(f0+[zeros(n,n_static) f1*gx zeros(n,nyf-n_both)]);
M2 = M1*f1;
dr.ghud = cell(M_.exo_det_length,1);
dr.ghud{1} = -M1*fudet;
for i = 2:M_.exo_det_length
dr.ghud{i} = -M2*dr.ghud{i-1}(end-nyf+1:end,:);
end
error('deterministic exogenous are not yet implemented in first order approximation for a block decomposed model');
% f1 = sparse(jacobia_(:,nonzeros(M_.lead_lag_incidence(M_.maximum_endo_lag+2:end,order_var))));
% f0 = sparse(jacobia_(:,nonzeros(M_.lead_lag_incidence(M_.maximum_endo_lag+1,order_var))));
% fudet = data(i).g1_xd;
% M1 = inv(f0+[zeros(n,n_static) f1*gx zeros(n,nyf-n_both)]);
% M2 = M1*f1;
% dr.ghud = cell(M_.exo_det_length,1);
% dr.ghud{1} = -M1*fudet;
% for i = 2:M_.exo_det_length
% dr.ghud{i} = -M2*dr.ghud{i-1}(end-nyf+1:end,:);
% end
end
%Endogeneous variables of the previous blocks
end
end;
if task ~=1
if (maximum_lag > 0)
lead_lag_incidence(maximum_lag+1, n_static+1:n_static + n_pred + n_both) - indexi_0 + 1
state_var = endo(lead_lag_incidence(maximum_lag+1, n_static+1:n_static + n_pred + n_both) - indexi_0 + 1);
[common_state_var, indx_common_dr_state_var, indx_common_state_var] = intersect(dr.state_var, state_var);
[diff_state_var, indx_diff_dr_state_var, indx_diff_state_var] = setxor(dr.state_var, state_var);
[union_state_var, indx_union_dr_state_var, indx_union_state_var] = union(dr.state_var, state_var);
[row_dr_ghx, col_dr_ghx] = size(dr.ghx);
ghx_new = zeros(row_dr_ghx + n, length(union_state_var));
ghx_new(1:row_dr_ghx, 1:col_dr_ghx) = dr.ghx;
ghx_new(row_dr_ghx + 1: row_dr_ghx + n, indx_common_dr_state_var) = ghx(:, indx_common_state_var);
ghx_new(row_dr_ghx + 1: row_dr_ghx + n, length(dr.state_var)+1:length(dr.state_var)+length(indx_diff_state_var)) = ghx(:, indx_diff_state_var);
dr.ghx = ghx_new;
dr.state_var = [dr.state_var state_var(indx_diff_state_var)];
if (maximum_lag > 0 && n_pred > 0)
sorted_col_dr_ghx = M_.block_structure.block(i).sorted_col_dr_ghx;
dr.ghx(endo, sorted_col_dr_ghx) = dr.ghx(endo, sorted_col_dr_ghx) + ghx;
data(i).ghx = ghx;
data(i).pol.i_ghx = sorted_col_dr_ghx;
else
data(i).pol.i_ghx = [];
end;
exo_var = exo;
[common_exo_var, indx_common_dr_exo_var, indx_common_exo_var] = intersect(dr.exo_var, exo_var);
[diff_exo_var, indx_diff_dr_exo_var, indx_diff_exo_var] = setxor(dr.exo_var, exo_var);
[union_exo_var, indx_union_dr_exo_var, indx_union_exo_var] = union(dr.exo_var, exo_var);
[row_dr_ghu, col_dr_ghu] = size(dr.ghu);
ghu_new = zeros(row_dr_ghu + exo_nbr, length(union_exo_var));
ghu_new(1:row_dr_ghu, 1:col_dr_ghu) = dr.ghu;
ghu_new(row_dr_ghu + 1: row_dr_ghu + n, indx_common_dr_exo_var) = ghu(:, indx_common_exo_var);
ghu_new(row_dr_ghu + 1: row_dr_ghu + n, length(dr.exo_var)+1:length(dr.exo_var)+length(indx_diff_exo_var)) = ghu(:, indx_diff_exo_var);
dr.ghu = ghu_new;
dr.exo_var = [dr.exo_var exo_var(indx_diff_exo_var)];
end
data(i).ghu = ghu;
dr.ghu(endo, exo) = ghu;
data(i).pol.i_ghu = exo;
end;
if (verbose)
disp('dr.ghx');
dr.ghx
disp('dr.ghu');
dr.ghu
end;
end;
M_.block_structure.block = data ;
if (verbose)
dr.ghx
dr.ghu
end;
disp('dr.ghx');
disp(real(dr.ghx));
disp('dr.ghu');
disp(real(dr.ghu));
end;
if (task == 1)
return;
end;
end;

12
matlab/dynare_estimation_1.m
View File

@ -143,7 +143,11 @@ if isequal(options_.mode_compute,0) && isempty(options_.mode_file) && options_.m
end
end
for i=bayestopt_.smoother_saved_var_list'
i1 = dr.order_var(bayestopt_.smoother_var_list(i));
if options_.block == 1
i1 = M_.block_structure.variable_reordered(bayestopt_.smoother_var_list(i));
else
i1 = dr.order_var(bayestopt_.smoother_var_list(i));
end;
eval(['oo_.SmoothedVariables.' deblank(M_.endo_names(i1,:)) ' = atT(i,:)'';']);
eval(['oo_.FilteredVariables.' deblank(M_.endo_names(i1,:)) ' = squeeze(aK(1,i,:));']);
eval(['oo_.UpdatedVariables.' deblank(M_.endo_names(i1,:)) ' = updated_variables(i,:)'';']);
@ -937,7 +941,11 @@ if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.psha
end
end
for i=bayestopt_.smoother_saved_var_list'
i1 = dr.order_var(bayestopt_.smoother_var_list(i));
if options_.block == 1
i1 = M_.block_structure.variable_reordered(bayestopt_.smoother_var_list(i));
else
i1 = dr.order_var(bayestopt_.smoother_var_list(i));
end;
eval(['oo_.SmoothedVariables.' deblank(M_.endo_names(i1,:)) ' = atT(i,:)'';']);
eval(['oo_.FilteredVariables.' deblank(M_.endo_names(i1,:)) ' = squeeze(aK(1,i,:));']);
eval(['oo_.UpdatedVariables.' deblank(M_.endo_names(i1,:)) ...

39
matlab/dynare_estimation_init.m
View File

@ -208,19 +208,32 @@ for i=1:n_varobs
k1 = [k1 strmatch(deblank(options_.varobs(i,:)),M_.endo_names, 'exact')];
end
% Define union of observed and state variables
k2 = union(var_obs_index',[dr.nstatic+1:dr.nstatic+dr.npred]', 'rows');
% Set restrict_state to postion of observed + state variables in expanded state vector.
oo_.dr.restrict_var_list = k2;
% set mf0 to positions of state variables in restricted state vector for likelihood computation.
[junk,bayestopt_.mf0] = ismember([dr.nstatic+1:dr.nstatic+dr.npred]',k2);
% Set mf1 to positions of observed variables in restricted state vector for likelihood computation.
[junk,bayestopt_.mf1] = ismember(var_obs_index,k2);
% Set mf2 to positions of observed variables in expanded state vector for filtering and smoothing.
bayestopt_.mf2 = var_obs_index;
bayestopt_.mfys = k1;
[junk,ic] = intersect(k2,nstatic+(1:npred)');
oo_.dr.restrict_columns = [ic; length(k2)+(1:nspred-npred)'];
if options_.block == 1
[k2, i_posA, i_posB] = union(k1', M_.state_var', 'rows');
% Set restrict_state to postion of observed + state variables in expanded state vector.
oo_.dr.restrict_var_list = [k1(i_posA) M_.state_var(sort(i_posB))];
% set mf0 to positions of state variables in restricted state vector for likelihood computation.
[junk,bayestopt_.mf0] = ismember(M_.state_var',oo_.dr.restrict_var_list);
% Set mf1 to positions of observed variables in restricted state vector for likelihood computation.
[junk,bayestopt_.mf1] = ismember(k1,oo_.dr.restrict_var_list);
% Set mf2 to positions of observed variables in expanded state vector for filtering and smoothing.
bayestopt_.mf2 = var_obs_index;
bayestopt_.mfys = k1;
oo_.dr.restrict_columns = [size(i_posA,1)+(1:size(M_.state_var,2))];
else
k2 = union(var_obs_index',[dr.nstatic+1:dr.nstatic+dr.npred]', 'rows');
% Set restrict_state to postion of observed + state variables in expanded state vector.
oo_.dr.restrict_var_list = k2;
% set mf0 to positions of state variables in restricted state vector for likelihood computation.
[junk,bayestopt_.mf0] = ismember([dr.nstatic+1:dr.nstatic+dr.npred]',k2);
% Set mf1 to positions of observed variables in restricted state vector for likelihood computation.
[junk,bayestopt_.mf1] = ismember(var_obs_index,k2);
% Set mf2 to positions of observed variables in expanded state vector for filtering and smoothing.
bayestopt_.mf2 = var_obs_index;
bayestopt_.mfys = k1;
[junk,ic] = intersect(k2,nstatic+(1:npred)');
oo_.dr.restrict_columns = [ic; length(k2)+(1:nspred-npred)'];
end;
k3 = [];
if options_.selected_variables_only

7
matlab/set_state_space.m
View File

@ -31,6 +31,7 @@ function dr=set_state_space(dr,M_)
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
global options_
max_lead = M_.maximum_endo_lead;
max_lag = M_.maximum_endo_lag;
@ -54,7 +55,11 @@ nboth = length(both_var);
npred = length(pred_var);
nfwrd = length(fwrd_var);
nstatic = length(stat_var);
order_var = [ stat_var(:); pred_var(:); both_var(:); fwrd_var(:)];
if options_.block == 1
order_var = M_.block_structure.variable_reordered;
else
order_var = [ stat_var(:); pred_var(:); both_var(:); fwrd_var(:)];
end;
inv_order_var(order_var) = (1:endo_nbr);
% building kmask for z state vector in t+1

7
matlab/simult_.m
View File

@ -67,8 +67,13 @@ if options_.k_order_solver% Call dynare++ routines.
y_(dr.order_var,:) = y_;
else
if options_.block
k2 = [dr.glb_pred dr.glb_both];
if M_.maximum_lag > 0
k2 = dr.state_var;
else
k2 = [];
end;
order_var = 1:M_.endo_nbr;
dr.order_var = order_var;
else
k2 = dr.kstate(find(dr.kstate(:,2) <= M_.maximum_lag+1),[1 2]);
k2 = k2(:,1)+(M_.maximum_lag+1-k2(:,2))*M_.endo_nbr;