 Usage:  {filtX,KG,PreviousPs} = kfilter2(y,mu,Sig,H,F,Q,R)  

 

 Input:



  y                      T x m matrix of observed time series, 

                         T is the number of observations, 

                         m is the dimension of time series 

                         

  mu                     n x 1 vector, the mean of the initial state 

                         

  Sig                    n x n covariance matrix of the initial state 

                         

  H                      m x n matrix 

                         

  F                      n x n matrix 

                         

  Q                      m x m variance-covariance matrix 

                         

  R                      n x n variance-covariance matrix 

                         

 Output:



  filtX                  T x n matrix of (Kalman-)filtered states x_{t|t}, 

                         T is the number of observations, 

                         n is the dimension of the states 

                         

  KG                     T x n x m "vector" of Kalman-Gain-matrices for different times 

                         

  PreviousPs             T x n x n "vector" of P_t|t's (Filter-Error-Covariances) 

                         

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(C) MD*TECH Method and Data Technologies, 21.9.2000

