mvaar

 Multivariate (Vector) adaptive AR estimation base on a multidimensional
 Kalman filer algorithm. A standard VAR model (A0=I) is implemented. The 
 state vector is defined as X=(A1|A2...|Ap) and x=vec(X')

 [x,e,Kalman,Q2] = mvaar(y,p,UC,mode,Kalman)

 The standard MVAR model is defined as:

        y(n)-A1(n)*y(n-1)-...-Ap(n)*y(n-p)=e(n)

    The dimension of y(n) equals s 
    
    Input Parameters:

         y            Observed data or signal 
         p            prescribed maximum model order (default 1)
        UC            update coefficient    (default 0.001)
        mode         update method of the process noise covariance matrix 0...4 ^
                    correspond to S0...S4 (default 0)

    Output Parameters

        e            prediction error of dimension s
        x            state vector of dimension s*s*p
        Q2            measurement noise covariance matrix of dimension s x s