Multivariate Subset Autoregression - Financial and Economic Forecasting (Chapter 3)

16 Pages Posted: 8 Jan 2003

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

This chapter uses a modified block Choleski decomposition method and tree pruning algorithms to attain the best multivariate subset autoregression for each size (number of non-zero coefficient matrices). Model selection criteria are then employed to select the optimum multivariate subset AR. A Monte Carlo study of these techniques has been investigated to assess their performance, and comparisons of computational efficiency of the proposed procedures are also provided.

Keywords: block Choleski decomposition, Hocking's 1-lag reduction algorithm, leaps and bounds algorithm

JEL Classification: C22, C53, E31

Suggested Citation

Penm, Jack and Penm, Jammie H. and Terrell, R. Deane, Multivariate Subset Autoregression - Financial and Economic Forecasting (Chapter 3) (October 2002). Available at SSRN: https://ssrn.com/abstract=358880 or http://dx.doi.org/10.2139/ssrn.358880

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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