Lag Length Estimation in Large Dimensional Systems

23 Pages Posted: 2 Dec 2002

See all articles by Jesús Gonzalo

Jesús Gonzalo

Universidad Carlos III de Madrid - Department of Statistics and Econometrics; Aarhus University - Department of Economics and Business Economics

Jean-Yves Pitarakis

University of Southampton - Division of Economics

Abstract

We study the impact of the system dimension on commonly used model selection criteria (AIC, BIC, HQ) and LR based general to specific testing strategies for lag length estimation in VARs. We show that AIC's well known overparameterization feature becomes quickly irrelevant as we move away from univariate models, with the criterion leading to consistent estimates under sufficiently large system dimensions. Unless the sample size is unrealistically small, all model selection criteria will tend to point towards low orders as the system dimension increases, with the AIC remaining by far the best performing criterion. This latter point is also illustrated via the use of an analytical power function for model selection criteria. The comparison between the model selection and general to specific testing strategy is discussed within the context of a new penalty term leading to the same choice of lag length under both approaches.

Keywords: Dimesionality, information criteria, lag length selection, VAR

Suggested Citation

Gonzalo Muñoz, Jesús and Pitarakis, Jean-Yves, Lag Length Estimation in Large Dimensional Systems. Available at SSRN: https://ssrn.com/abstract=314483

Jesús Gonzalo Muñoz (Contact Author)

Universidad Carlos III de Madrid - Department of Statistics and Econometrics ( email )

c/ Madrid 126
Getafe (Madrid), 28903
Spain
34 + 91 624 9853 (Phone)
34 + 91 624 9849 (Fax)

Aarhus University - Department of Economics and Business Economics

Fuglesangs Allé 4
Aarhus V
Denmark

Jean-Yves Pitarakis

University of Southampton - Division of Economics ( email )

Southampton, SO17 1BJ
United Kingdom
+44-23-80592631 (Phone)
+44-23-80593858 (Fax)

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