Added files for extended path simulations.
git-svn-id: https://www.dynare.org/svn/dynare/trunk@3120 ac1d8469-bf42-47a9-8791-bf33cf982152time-shift
stepan
parent
ba6c1da0ee
commit
3a137c3eaa
|
|
@ -0,0 +1,40 @@
|
|||
function d = bksup0(c,ny,jcf,iyf,icf,periods)
|
||||
% Solves deterministic models recursively by backsubstitution for one lead/lag
|
||||
%
|
||||
% INPUTS
|
||||
% ny: number of endogenous variables
|
||||
% jcf: variables index forward
|
||||
%
|
||||
% OUTPUTS
|
||||
% d: vector of backsubstitution results
|
||||
%
|
||||
% SPECIAL REQUIREMENTS
|
||||
% none
|
||||
|
||||
% Copyright (C) 2003-2009 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
% Dynare is free software: you can redistribute it and/or modify
|
||||
% it under the terms of the GNU General Public License as published by
|
||||
% the Free Software Foundation, either version 3 of the License, or
|
||||
% (at your option) any later version.
|
||||
%
|
||||
% Dynare is distributed in the hope that it will be useful,
|
||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
% GNU General Public License for more details.
|
||||
%
|
||||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||
|
||||
ir = ((periods-2)*ny+1):(ny+(periods-2)*ny);
|
||||
irf = iyf+(periods-1)*ny ;
|
||||
|
||||
for i = 2:periods
|
||||
c(ir,jcf) = c(ir,jcf)-c(ir,icf)*c(irf,jcf);
|
||||
ir = ir-ny;
|
||||
irf = irf-ny;
|
||||
end
|
||||
|
||||
d = c(:,jcf);
|
||||
|
|
@ -0,0 +1,67 @@
|
|||
function time_series = extended_path(initial_conditions,sample_size)
|
||||
% Stochastic simulation of a non linear DSGE model using the Extended Path method (Fair and Taylor 1983). A time
|
||||
% series of size T is obtained by solving T perfect foresight models.
|
||||
%
|
||||
% INPUTS
|
||||
% o initial_conditions [double] m*nlags array, where m is the number of endogenous variables in the model and
|
||||
% nlags is the maximum number of lags.
|
||||
% o sample_size [integer] scalar, size of the sample to be simulated.
|
||||
%
|
||||
% OUTPUTS
|
||||
% o time_series [double] m*sample_size array, the simulations.
|
||||
%
|
||||
% ALGORITHM
|
||||
%
|
||||
% SPECIAL REQUIREMENTS
|
||||
|
||||
% Copyright (C) 2009 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
% Dynare is free software: you can redistribute it and/or modify
|
||||
% it under the terms of the GNU General Public License as published by
|
||||
% the Free Software Foundation, either version 3 of the License, or
|
||||
% (at your option) any later version.
|
||||
%
|
||||
% Dynare is distributed in the hope that it will be useful,
|
||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
% GNU General Public License for more details.
|
||||
%
|
||||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||
global M_ oo_ options_
|
||||
|
||||
% Set default initial conditions.
|
||||
if isempty(initial_conditions)
|
||||
initial_conditions = repmat(oo_.steady_state,1,M_.maximum_lag);
|
||||
end
|
||||
|
||||
% Copy sample_size to periods.
|
||||
options_.periods = sample_size;
|
||||
|
||||
% Initialize the exogenous variables.
|
||||
make_ex_;
|
||||
|
||||
% Initialize the endogenous variables.
|
||||
make_y_;
|
||||
|
||||
% Initialize the output array.
|
||||
time_series = NaN(M_.endo_nbr,sample_size+1);
|
||||
|
||||
% Set the covariance matrix of the structural innovations.
|
||||
variances = diag(M_.Sigma_e);
|
||||
positive_var_indx = find(variances>0);
|
||||
covariance_matrix = M_.Sigma_e(positive_var_indx,positive_var_indx);
|
||||
number_of_structural_innovations = length(covariance_matrix);
|
||||
covariance_matrix_upper_cholesky = chol(covariance_matrix);
|
||||
|
||||
tdx = M_.maximum_lag+1;
|
||||
|
||||
for t=1:sample_size
|
||||
oo_.exo_simul(tdx,positive_var_indx) = exp(randn(1,number_of_structural_innovations)*covariance_matrix_upper_cholesky-.5*variances(positive_var_indx)');
|
||||
perfect_foresight_simulation;
|
||||
time_series(:,t+1) = oo_.endo_simul(:,tdx);
|
||||
oo_.endo_simul(:,1:end-1) = oo_.endo_simul(:,2:end);
|
||||
oo_.endo_simul(:,end) = oo_.steady_state;
|
||||
end
|
||||
|
|
@ -0,0 +1,135 @@
|
|||
function info = perfect_foresight_simulation(init)
|
||||
% performs deterministic simulations with lead or lag on one period
|
||||
%
|
||||
% INPUTS
|
||||
% none
|
||||
%
|
||||
% OUTPUTS
|
||||
% none
|
||||
%
|
||||
% ALGORITHM
|
||||
% Laffargue, Boucekkine, Juillard (LBJ)
|
||||
% see Juillard (1996) Dynare: A program for the resolution and
|
||||
% simulation of dynamic models with forward variables through the use
|
||||
% of a relaxation algorithm. CEPREMAP. Couverture Orange. 9602.
|
||||
%
|
||||
% SPECIAL REQUIREMENTS
|
||||
% None.
|
||||
|
||||
% Copyright (C) 1996-2009 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
% Dynare is free software: you can redistribute it and/or modify
|
||||
% it under the terms of the GNU General Public License as published by
|
||||
% the Free Software Foundation, either version 3 of the License, or
|
||||
% (at your option) any later version.
|
||||
%
|
||||
% Dynare is distributed in the hope that it will be useful,
|
||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
% GNU General Public License for more details.
|
||||
%
|
||||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||
|
||||
global M_ options_ oo_
|
||||
global ct_ it_
|
||||
|
||||
persistent flag_init
|
||||
persistent lead_lag_incidence dynamic_model ny nyp nyf nrs nrc iyf iyp isp is isf isf1 iz icf
|
||||
|
||||
if nargin==1
|
||||
flag_init = [];
|
||||
end
|
||||
|
||||
if isempty(flag_init)
|
||||
lead_lag_incidence = M_.lead_lag_incidence;
|
||||
dynamic_model = [M_.fname '_dynamic'];
|
||||
ny = size(oo_.endo_simul,1);
|
||||
nyp = nnz(lead_lag_incidence(1,:));
|
||||
nyf = nnz(lead_lag_incidence(3,:));
|
||||
nrs = ny+nyp+nyf+1;
|
||||
nrc = nyf+1;
|
||||
iyf = find(lead_lag_incidence(3,:)>0);
|
||||
iyp = find(lead_lag_incidence(1,:)>0);
|
||||
isp = 1:nyp;
|
||||
is = (nyp+1):(ny+nyp);
|
||||
isf = iyf+nyp;
|
||||
isf1 = (nyp+ny+1):(nyf+nyp+ny+1);
|
||||
iz = 1:(ny+nyp+nyf);
|
||||
icf = 1:size(iyf,2);
|
||||
flag_init = 1;
|
||||
if nargin==1
|
||||
return
|
||||
end
|
||||
end
|
||||
|
||||
endo_simul = oo_.endo_simul;
|
||||
periods = options_.periods;
|
||||
|
||||
stop = 0 ;
|
||||
it_init = M_.maximum_lag+1;
|
||||
|
||||
info.convergence = 1;
|
||||
info.time = 0;
|
||||
info.error = 0;
|
||||
info.iterations.time = zeros(options_.maxit_,1);
|
||||
info.iterations.error = info.iterations.time;
|
||||
|
||||
h1 = clock;
|
||||
for iter = 1:options_.maxit_
|
||||
h2 = clock;
|
||||
if ct_ == 0
|
||||
c = zeros(ny*periods,nrc);
|
||||
else
|
||||
c = zeros(ny*(periods+1),nrc);
|
||||
end
|
||||
it_ = it_init ;
|
||||
z = [ endo_simul(iyp,it_-1) ; endo_simul(:,it_) ; endo_simul(iyf,it_+1) ];
|
||||
[d1,jacobian] = feval(dynamic_model,z,oo_.exo_simul, M_.params, it_);
|
||||
jacobian = [jacobian(:,iz) , -d1];
|
||||
ic = 1:ny;
|
||||
icp = iyp;
|
||||
c(ic,:) = jacobian(:,is)\jacobian(:,isf1) ;
|
||||
for it_ = it_init+(1:periods-1)
|
||||
z = [ endo_simul(iyp,it_-1) ; endo_simul(:,it_) ; endo_simul(iyf,it_+1)];
|
||||
[d1,jacobian] = feval(dynamic_model,z,oo_.exo_simul, M_.params, it_);
|
||||
jacobian = [jacobian(:,iz) , -d1];
|
||||
jacobian(:,[isf nrs]) = jacobian(:,[isf nrs])-jacobian(:,isp)*c(icp,:);
|
||||
ic = ic + ny;
|
||||
icp = icp + ny;
|
||||
c(ic,:) = jacobian(:,is)\jacobian(:,isf1);
|
||||
end
|
||||
if ct_ == 1
|
||||
s = eye(ny);
|
||||
s(:,isf) = s(:,isf)+c(ic,1:nyf);
|
||||
ic = ic + ny;
|
||||
c(ic,nrc) = s\c(:,nrc);
|
||||
c = bksup0(c,ny,nrc,iyf,icf,periods);
|
||||
c = reshape(c,ny,periods+1);
|
||||
endo_simul(:,it_init+(0:periods)) = endo_simul(:,it_init+(0:periods))+options_.slowc*c;
|
||||
else
|
||||
c = bksup0(c,ny,nrc,iyf,icf,periods);
|
||||
c = reshape(c,ny,periods);
|
||||
endo_simul(:,it_init+(0:periods-1)) = endo_simul(:,it_init+(0:periods-1))+options_.slowc*c;
|
||||
end
|
||||
err = max(max(abs(c./options_.scalv')));
|
||||
info.iterations.time(iter) = etime(clock,h2);
|
||||
info.iterations.error(iter) = err;
|
||||
if err < options_.dynatol
|
||||
stop = 1;
|
||||
info.time = etime(clock,h1);
|
||||
info.error = err;
|
||||
info.iterations.time = info.iterations.time(1:iter);
|
||||
info.iterations.error = info.iterations.error(1:iter);
|
||||
oo_.endo_simul = endo_simul;
|
||||
break
|
||||
end
|
||||
end
|
||||
|
||||
if ~stop
|
||||
info.time = etime(clock,h1);
|
||||
info.error = err;
|
||||
info.convergence = 0;
|
||||
end
|
||||
Loading…
Reference in New Issue