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Merge branch 'kalman_headers' of git.dynare.org:JohannesPfeifer/dynare

time-shift
Sébastien Villemot 2021-01-19 17:29:24 +01:00
parent 315bfa6f51 869147c13a
commit cc38c4e9f7
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GPG Key ID: 2CECE9350ECEBE4A
5 changed files with 11 additions and 10 deletions
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5
matlab/kalman/likelihood/kalman_filter_d.m
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@ -5,7 +5,7 @@ function [dLIK,dlik,a,Pstar] = kalman_filter_d(Y, start, last, a, Pinf, Pstar, k
% Y [double] pp*smpl matrix of (detrended) data, where pp is the number of observed variables.
% start [integer] scalar, first observation.
% last [integer] scalar, last observation.
% a [double] mm*1 vector, levels of the state variables.
% a [double] mm*1 vector, levels of the predicted initial state variables (E_{0}(alpha_1)).
% Pinf [double] mm*mm matrix used to initialize the covariance matrix of the state vector.
% Pstar [double] mm*mm matrix used to initialize the covariance matrix of the state vector.
% kalman_tol [double] scalar, tolerance parameter (rcond) of F_star.
@ -25,7 +25,8 @@ function [dLIK,dlik,a,Pstar] = kalman_filter_d(Y, start, last, a, Pinf, Pstar, k
% OUTPUTS
% LIK [double] scalar, minus loglikelihood
% lik [double] smpl*1 vector, log density of each vector of observations.
% a [double] mm*1 vector, current estimate of the state vector.
% a [double] mm*1 vector, current estimate of the state vector tomorrow
% (E_{T}(alpha_{T+1})).
% Pstar [double] mm*mm matrix, covariance matrix of the state vector.
%
% REFERENCES

4
matlab/kalman/likelihood/kalman_filter_fast.m
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@ -20,7 +20,7 @@ function [LIK, LIKK, a, P] = kalman_filter_fast(Y,start,last,a,P,kalman_tol,ricc
%! @item last
%! Integer scalar, last period (@var{last}-@var{first} has to be inferior to T).
%! @item a
%! Vector (@var{mm}*1) of doubles, initial mean of the state vector.
%! Vector (@var{mm}*1) of doubles, levels of the predicted initial state variables (E_{0}(alpha_1)).
%! @item P
%! Matrix (@var{mm}*@var{mm}) of doubles, initial covariance matrix of the state vector.
%! @item kalman_tol
@ -59,7 +59,7 @@ function [LIK, LIKK, a, P] = kalman_filter_fast(Y,start,last,a,P,kalman_tol,ricc
%! @item likk
%! Column vector of doubles, values of the density of each observation.
%! @item a
%! Vector (@var{mm}*1) of doubles, mean of the state vector at the end of the (sub)sample.
%! Vector (@var{mm}*1) of doubles, mean of the state vector at the end of the (sub)sample (E_{T}(alpha_{T+1})).
%! @item P
%! Matrix (@var{mm}*@var{mm}) of doubles, covariance of the state vector at the end of the (sub)sample.
%! @end table

4
matlab/kalman/likelihood/kalman_filter_ss.m
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@ -17,7 +17,7 @@ function [LIK, likk, a] = kalman_filter_ss(Y,start,last,a,T,K,iF,log_dF,Z,pp,Zfl
%! @item last
%! Integer scalar, last period (@var{last}-@var{first} has to be inferior to T).
%! @item a
%! Vector (mm*1) of doubles, initial mean of the state vector.
%! Vector (mm*1) of doubles, levels of the predicted initial state variables (E_{0}(alpha_1)).
%! @item T
%! Matrix (mm*mm) of doubles, transition matrix of the state equation.
%! @item K
@ -42,7 +42,7 @@ function [LIK, likk, a] = kalman_filter_ss(Y,start,last,a,T,K,iF,log_dF,Z,pp,Zfl
%! @item likk
%! Column vector of doubles, values of the density of each observation.
%! @item a
%! Vector (mm*1) of doubles, mean of the state vector at the end of the (sub)sample.
%! Vector (mm*1) of doubles, current estimate of the state vector tomorrow (E_{T}(alpha_{T+1})).
%! @end table
%! @sp 2
%! @strong{This function is called by:}

4
matlab/kalman/likelihood/missing_observations_kalman_filter.m
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@ -8,7 +8,7 @@ function [LIK, lik, a, P] = missing_observations_kalman_filter(data_index,numbe
% Y [double] pp*smpl matrix of data.
% start [integer] scalar, index of the first observation.
% last [integer] scalar, index of the last observation.
% a [double] pp*1 vector, initial level of the state vector.
% a [double] pp*1 vector, levels of the predicted initial state variables (E_{0}(alpha_1)).
% P [double] pp*pp matrix, covariance matrix of the initial state vector.
% kalman_tol [double] scalar, tolerance parameter (rcond).
% riccati_tol [double] scalar, tolerance parameter (riccati iteration).
@ -25,7 +25,7 @@ function [LIK, lik, a, P] = missing_observations_kalman_filter(data_index,numbe
% OUTPUTS
% LIK [double] scalar, MINUS loglikelihood
% lik [double] vector, density of observations in each period.
% a [double] mm*1 vector, estimated level of the states.
% a [double] mm*1 vector, current estimate of the state vector tomorrow (E_{T}(alpha_{T+1})).
% P [double] mm*mm matrix, covariance matrix of the states.
%
%

4
matlab/kalman/likelihood/missing_observations_kalman_filter_d.m
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@ -12,7 +12,7 @@ function [dLIK,dlik,a,Pstar] = missing_observations_kalman_filter_d(data_index,n
% Y [double] pp*smpl matrix of (detrended) data, where pp is the number of observed variables.
% start [integer] scalar, first observation.
% last [integer] scalar, last observation.
% a [double] mm*1 vector, levels of the state variables.
% a [double] mm*1 vector, levels of the predicted initial state variables (E_{0}(alpha_1)).
% Pinf [double] mm*mm matrix used to initialize the covariance matrix of the state vector.
% Pstar [double] mm*mm matrix used to initialize the covariance matrix of the state vector.
% kalman_tol [double] scalar, tolerance parameter (rcond).
@ -30,7 +30,7 @@ function [dLIK,dlik,a,Pstar] = missing_observations_kalman_filter_d(data_index,n
% OUTPUTS
% dLIK [double] scalar, MINUS loglikelihood
% dlik [double] vector, density of observations in each period.
% a [double] mm*1 vector, estimated level of the states.
% a [double] mm*1 vector, current estimate of the state vector tomorrow (E_{T}(alpha_{T+1})).
% Pstar [double] mm*mm matrix, covariance matrix of the states.
%
% REFERENCES