42 lines
1.8 KiB
Matlab
42 lines
1.8 KiB
Matlab
%
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% SYNOPSIS
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%
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% [loglik,per,d,alpha,eta,V] = kalman_smoother(Z,H,T,R,Q,Y,a,P)
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% [loglik,per,d,alpha,eta,V] = kalman_smoother(Z,H,T,R,Q,Y,a,P,flag)
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% [loglik,per,d,alpha,eta,V] = kalman_smoother(Z,H,T,R,Q,Y,a,Pstar,Pinf)
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% [loglik,per,d,alpha,eta,V] = kalman_smoother(Z,H,T,R,Q,Y,a,Pstar,Pinf,flag)
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%
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% SEMANTICS
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%
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% The first two commands run a Kalman filter and smoother for non-diffuse initial
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% conditions, the other two for diffuse initial conditions.
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%
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% Input:
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% Z,H,T,R,Q gives a state space form
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% Y observed data (columns correspond to periods)
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% a mean of initial state
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% P covariance of initial non-diffuse state
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% Pstar finite part of covariance of initial diffuse state
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% Pinf infinite part of covariance of initial diffuse state
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% flag string starting with 'u', or 'U' runs a univariate
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% form of the filter; if omitted, a multivariate version
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% is run by default
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%
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% Output:
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% loglik data log likelihood
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% per number of succesfully filtered periods; if no error
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% then per equals to the number of columns of Y
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% d number of initial periods for which the state is
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% still diffuse (d is always 0 for non-diffuse case)
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% alpha matrix of smoothed states; columns are periods
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% eta matrix of smoothed shocks; columns are periods
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% V 3D array of smoothed state variances; V(:,:,t) is
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% smoothed state variance-covariance at time t
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%
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% Copyright 2005, Ondra Kamenik
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%
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function [loglik,per,d,alpha,eta,V] = kalman_smoother(varargin)
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[loglik,per,d,alpha,eta,V] = kalman_smoother_(varargin{:});
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