ARFIT2 estimates multivariate autoregressive parameters
 of the MVAR process Y

   Y(t,:)' = w' + A1*Y(t-1,:)' + ... + Ap*Y(t-p,:)' + x(t,:)'

 ARFIT2 uses the Nutall-Strand method (multivariate Burg algorithm) 
 which provides better estimates the ARFIT [1], and uses the 
 same arguments. Moreover, ARFIT2 is faster and can deal with 
 missing values encoded as NaNs. 

 [w, A, C, sbc, fpe] = arfit2(v, pmin, pmax, selector, no_const)

 INPUT: 
  v		data - each channel in a column
  pmin, pmax 	minimum and maximum model order
  selector	'fpe' or 'sbc' [default] 
  no_const	'zero' indicates no bias/offset need to be estimated 
		in this case is w = [0, 0, ..., 0]'; 

 OUTPUT: 
  w		mean of innovation noise
  A		[A1,A2,...,Ap] MVAR estimates	
  C		covariance matrix of innovation noise
  sbc, fpe	criteria for model order selection 

 see also: ARFIT, MVAR

 REFERENCES:
  [1] A. Schloegl, 2006, Comparison of Multivariate Autoregressive Estimators.
       Signal processing, p. 2426-9.
  [2] T. Schneider and A. Neumaier, 2001. 
	Algorithm 808: ARFIT-a Matlab package for the estimation of parameters and eigenmodes 
	of multivariate autoregressive models. ACM-Transactions on Mathematical Software. 27, (Mar.), 58-65.

Package: tsa