From e55f3fd272888dc59d2ac5f44cbdb3b54c08b172 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Fr=C3=A9d=C3=A9ric=20Karam=C3=A9?=
<frederic.karame@univ-lemans.fr>
Date: Fri, 14 Nov 2014 18:16:11 +0100
Subject: [PATCH] Adds a modification to work with order 1 too.
---
src/solve_model_for_online_filter.m | 62 ++++++++++++++++-------------
1 file changed, 34 insertions(+), 28 deletions(-)
diff --git a/src/solve_model_for_online_filter.m b/src/solve_model_for_online_filter.m
index a9cefe4..0d8f191 100644
--- a/src/solve_model_for_online_filter.m
+++ b/src/solve_model_for_online_filter.m
@@ -134,7 +134,8 @@ trend_coeff = [];
exit_flag = 1;
% Set the number of observed variables
-nvobs = DynareDataset.info.nvobs;
+%nvobs = DynareDataset.info.nvobs;
+nvobs = size(DynareDataset.data,1) ;
%------------------------------------------------------------------------------
% 1. Get the structural parameters & define penalties
@@ -240,15 +241,13 @@ Model.H = H;
% Linearize the model around the deterministic sdteadystate and extract the matrices of the state equation (T and R).
[T,R,SteadyState,info,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults,'restrict');
-%if info(1) == 1 || info(1) == 2 || info(1) == 5
-% fval = objective_function_penalty_base+1;
-% exit_flag = 0;
-% return
-%elseif info(1) == 3 || info(1) == 4 || info(1)==6 ||info(1) == 19 || info(1) == 20 || info(1) == 21
-% fval = objective_function_penalty_base+info(2);
-% exit_flag = 0;
-% return
-%end
+%disp(info)
+
+if info(1) ~= 0
+ ReducedForm = 0 ;
+ exit_flag = 55;
+ return
+end
% Define a vector of indices for the observed variables. Is this really usefull?...
BayesInfo.mf = BayesInfo.mf1;
@@ -265,24 +264,24 @@ else
end
% Define the deterministic linear trend of the measurement equation.
-if BayesInfo.with_trend
- trend_coeff = zeros(DynareDataset.info.nvobs,1);
- t = DynareOptions.trend_coeffs;
- for i=1:length(t)
- if ~isempty(t{i})
- trend_coeff(i) = evalin('base',t{i});
- end
- end
- trend = repmat(constant,1,DynareDataset.info.ntobs)+trend_coeff*[1:DynareDataset.info.ntobs];
-else
- trend = repmat(constant,1,DynareDataset.info.ntobs);
-end
+%if BayesInfo.with_trend
+% trend_coeff = zeros(DynareDataset.info.nvobs,1);
+% t = DynareOptions.trend_coeffs;
+% for i=1:length(t)
+% if ~isempty(t{i})
+% trend_coeff(i) = evalin('base',t{i});
+% end
+% end
+% trend = repmat(constant,1,DynareDataset.info.ntobs)+trend_coeff*[1:DynareDataset.info.ntobs];
+%else
+% trend = repmat(constant,1,DynareDataset.info.ntobs);
+%end
% Get needed informations for kalman filter routines.
start = DynareOptions.presample+1;
np = size(T,1);
mf = BayesInfo.mf;
-Y = transpose(DynareDataset.rawdata);
+Y = transpose(DynareDataset.data);
%------------------------------------------------------------------------------
% 3. Initial condition of the Kalman filter
@@ -307,11 +306,18 @@ end
ReducedForm.ghx = dr.ghx(restrict_variables_idx,:);
ReducedForm.ghu = dr.ghu(restrict_variables_idx,:);
-ReducedForm.ghxx = dr.ghxx(restrict_variables_idx,:);
-ReducedForm.ghuu = dr.ghuu(restrict_variables_idx,:);
-ReducedForm.ghxu = dr.ghxu(restrict_variables_idx,:);
ReducedForm.steadystate = dr.ys(dr.order_var(restrict_variables_idx));
-ReducedForm.constant = ReducedForm.steadystate + .5*dr.ghs2(restrict_variables_idx);
+if DynareOptions.order>1
+ ReducedForm.ghxx = dr.ghxx(restrict_variables_idx,:);
+ ReducedForm.ghuu = dr.ghuu(restrict_variables_idx,:);
+ ReducedForm.ghxu = dr.ghxu(restrict_variables_idx,:);
+ ReducedForm.constant = ReducedForm.steadystate + .5*dr.ghs2(restrict_variables_idx);
+else
+ ReducedForm.ghxx = zeros(size(restrict_variables_idx,1),size(dr.kstate,2));
+ ReducedForm.ghuu = zeros(size(restrict_variables_idx,1),size(dr.ghu,2));
+ ReducedForm.ghxu = zeros(size(restrict_variables_idx,1),size(dr.ghx,2));
+ ReducedForm.constant = ReducedForm.steadystate ;
+end
ReducedForm.state_variables_steady_state = dr.ys(dr.order_var(state_variables_idx));
ReducedForm.Q = Q;
ReducedForm.H = H;
@@ -323,7 +329,7 @@ if observation_number==1
switch DynareOptions.particle.initialization
case 1% Initial state vector covariance is the ergodic variance associated to the first order Taylor-approximation of the model.
StateVectorMean = ReducedForm.constant(mf0);
- StateVectorVariance = lyapunov_symm(ReducedForm.ghx(mf0,:),ReducedForm.ghu(mf0,:)*ReducedForm.Q*ReducedForm.ghu(mf0,:)',1e-12,1e-12,[],[],DynareOptions.debug);
+ StateVectorVariance = lyapunov_symm(ReducedForm.ghx(mf0,:),ReducedForm.ghu(mf0,:)*ReducedForm.Q*ReducedForm.ghu(mf0,:)',1e-12,1e-12);
case 2% Initial state vector covariance is a monte-carlo based estimate of the ergodic variance (consistent with a k-order Taylor-approximation of the model).
StateVectorMean = ReducedForm.constant(mf0);
old_DynareOptionsperiods = DynareOptions.periods;
--
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