A Note on the Recursions of Multichannel Complex Subset Autoregressions - Financial and Economic Forecasting (Chapter 2)

10 Pages Posted: 19 Dec 2002

See all articles by Jack H.W. Penm

Jack H.W. Penm

Australian National University - School of Finance and Applied Statistics, Faculty of Economics and Commerce

Jammie H. Penm

Independent

R. Deane Terrell

Australian National University (ANU) - National Graduate School of Management

Date Written: October 2002

Abstract

The recursive algorithm to select the optimum multivariate real subset autoregressive model (AR) [1] is generalized to apply to multichannel complex subset AR's. It is initiated by fitting all 'forward' and 'backward' one-lag AR's. The method then allows one to develop successively all complex subset AR's of size (the number of lags with nonzero coefficient matrices) from 1 to K. Finally, the best subsets of each size with the minimum generalized residual power for that size are compared to any one of three model selection criteria to find the optimum multichannel complex subset AR.

Keywords: Subset AR, recursions, model selection criteria

JEL Classification: C10, C63

Suggested Citation

Penm, Jack and Penm, Jammie H. and Terrell, R. Deane, A Note on the Recursions of Multichannel Complex Subset Autoregressions - Financial and Economic Forecasting (Chapter 2) (October 2002). Available at SSRN: https://ssrn.com/abstract=354960 or http://dx.doi.org/10.2139/ssrn.354960

Jack Penm (Contact Author)

Australian National University - School of Finance and Applied Statistics, Faculty of Economics and Commerce ( email )

Canberra, Australian Capital Territory 0200
Australia
+61 (02) 61250535 (Phone)
+61 (02) 61250087 (Fax)

Jammie H. Penm

Independent ( email )

61 (02) 62880126 (Phone)
61 (02) 61250087 (Fax)

R. Deane Terrell

Australian National University (ANU) - National Graduate School of Management ( email )

Sir Roland Wilson Building (120)
Canberra, Australian Capital Territory 0200
Australia

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