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

Package: tsa