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Fixed bugs. Changed internal documentation.

time-shift
Stéphane Adjemian (Charybdis) 2011-11-24 18:00:40 +01:00
parent 6105ed433c
commit 16d2fd5673
1 changed files with 27 additions and 27 deletions
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54
matlab/particle/local_state_equation_2.m
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@ -1,12 +1,14 @@
function [y,y_] = local_state_iteration(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,ghxu,yhat_,ss)
function [y,y_] = local_state_equation_2(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,ghxu,yhat_,ss)
%@info:
%! @deftypefn {Function File} {@var{y}, @var{y_} =} local_state_iteration (@var{yhat},@var{epsilon}, @var{ghx}, @var{ghu}, @var{constant}, @var{ghxx}, @var{ghuu}, @var{ghxu}, @var{yhat_}, @var{ss})
%! @anchor{particle/local_state_iteration}
%! @deftypefn {Function File} {@var{y}, @var{y_} =} local_state_equation_2 (@var{yhat},@var{epsilon}, @var{ghx}, @var{ghu}, @var{constant}, @var{ghxx}, @var{ghuu}, @var{ghxu}, @var{yhat_}, @var{ss})
%! @anchor{particle/local_state_equation_2}
%! @sp 1
%! Given an initial condition (y) and an innovation (epsilon), this routines computes the next value of the state variables if the
%! Given the states (y) and structural innovations (epsilon), this routine computes the level of selected endogenous variables when the
%! model is approximated by an order two taylor expansion around the deterministic steady state. Depending on the number of input/output
%! argument the pruning algorithm advocated by C. Sims is used or not.
%! argument the pruning algorithm advocated by C. Sims is used or not (this version should not be used if the selected endogenous variables
%! are not the states of the model).
%!
%! @sp 2
%! @strong{Inputs}
%! @sp 1
@ -16,30 +18,30 @@ function [y,y_] = local_state_iteration(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,
%! @item epsilon
%! q*1 vector of doubles, structural innovations.
%! @item ghx
%! n*n matrix of doubles, is a subset of dr.ghx where we only consider the lines corresponding to the state variables.
%! m*n matrix of doubles, restricted dr.ghx where we only consider the lines corresponding to a subset of endogenous variables.
%! @item ghu
%! n*q matrix of doubles, is a subset of dr.ghu where we only consider the lines corresponding to the state variables.
%! m*q matrix of doubles, restricted dr.ghu where we only consider the lines corresponding to a subset of endogenous variables.
%! @item constant
%! n*1 vector of doubles, deterministic steady state plus second order correction for the state variables.
%! m*1 vector of doubles, deterministic steady state plus second order correction for a subset of endogenous variables.
%! @item ghxx
%! n*n² matrix of doubles, subset of dr.ghxx where we only consider the lines corresponding to the state variables.
%! m*n² matrix of doubles, restricted dr.ghxx where we only consider the lines corresponding to a subset of endogenous variables.
%! @item ghuu
%! n*q² matrix of doubles, subset of dr.ghuu where we only consider the lines corresponding to the state variables.
%! m*q² matrix of doubles, restricted dr.ghuu where we only consider the lines corresponding to a subset of endogenous variables.
%! @item ghxu
%! n*(nq) matrix of doubles, subset of dr.ghxu where we only consider the lines corresponding to the state variables.
%! m*(nq) matrix of doubles, subset of dr.ghxu where we only consider the lines corresponding to a subset of endogenous variables.
%! @item yhat_
%! n*1 vector of doubles, second initial condition for pruning version.
%! n*1 vector of doubles, spurious states for the pruning version.
%! @item ss
%! n*1 vector of doubles, steady state for the union of the states and observed variables.
%! n*1 vector of doubles, steady state for the states.
%! @end table
%! @sp 2
%! @strong{Outputs}
%! @sp 1
%! @table @ @var
%! @item y
%! n*1 vector of doubles, next values for the state variables.
%! m*1 vector of doubles, selected endogenous variables.
%! @item y_
%! n*1 vector of doubles, update of the latent variables needed for the pruning version (first order update).
%! m*1 vector of doubles, update of the latent variables needed for the pruning version (first order update).
%! @end table
%! @sp 2
%! @strong{Remarks}
@ -57,8 +59,6 @@ function [y,y_] = local_state_iteration(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,
%@eod:
% Copyright (C) 2011 Dynare Team
% stephane DOT adjemian AT univ DASH lemans DOT fr
% frederic DOT karame AT univ DASH evry DOT fr
%
% This file is part of Dynare.
%
@ -75,20 +75,23 @@ function [y,y_] = local_state_iteration(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
% AUTHOR(S) stephane DOT adjemian AT univ DASH lemans DOT fr
% frederic DOT karame AT univ DASH evry DOT fr
number_of_threads = 1;
if nargin==8
pruning = 0;
if nargout>1
error('local_state_iteration:: Numbers of input and output argument are inconsistent!')
error('local_state_equation_2:: Numbers of input and output argument are inconsistent!')
end
elseif nargin==10
pruning = 1;
if nargout~=2
error('local_state_iteration:: Numbers of input and output argument are inconsistent!')
error('local_state_equation_2:: Numbers of input and output argument are inconsistent!')
end
else
error('local_state_iteration:: Wrong number of input arguments!')
error('local_state_equation_2:: Wrong number of input arguments!')
end
switch pruning
@ -111,8 +114,6 @@ switch pruning
end
%@test:1
%$ addpath ../matlab
%$
%$ n = 2;
%$ q = 3;
%$
@ -128,8 +129,8 @@ end
%$ ss = ones(n,1);
%$
%$ % Call the tested routine.
%$ y1 = local_state_iteration(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,ghxu);
%$ [y2,y2_] = local_state_iteration(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,ghxu,yhat_,ss);
%$ y1 = local_state_equation_2(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,ghxu);
%$ [y2,y2_] = local_state_equation_2(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,ghxu,yhat_,ss);
%$
%$ % Check the results.
%$ t(1) = dyn_assert(y1,ones(n,1));
@ -139,7 +140,6 @@ end
%@eof:1
%@test:2
%$ addpath ../matlab
%$ old_path = pwd;
%$ cd([fileparts(which('dynare')) '/../tests/']);
%$ dynare('dsge_base2');
@ -163,8 +163,8 @@ end
%$ ss = dr.ys(istates,:);
%$
%$ % Call the tested routine.
%$ y1 = local_state_iteration(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,ghxu);
%$ [y2,y2_] = local_state_iteration(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,ghxu,yhat_,ss);
%$ y1 = local_state_equation_2(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,ghxu);
%$ [y2,y2_] = local_state_equation_2(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,ghxu,yhat_,ss);
%$
%$ % Check the results.
%$ t(1) = 1;%dyn_assert(y1,ones(n,1));