Lag Length Estimation in Large Dimensional Systems

Jesus Gonzalo () and Jean-Yves Pitarakis ()

Econometrics from EconWPA

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 VAR's. 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: Dimensionality; Information Criteria; Lag Length Selection; VAR (search for similar items in EconPapers)
JEL-codes: C32 C52 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm
Date: 2001-08-31
Note: Type of Document - Adobe pdf; prepared on IBM PC - ; pages: 26
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Related works:
Working Paper: Lag Length Estimation in Large Dimensional Systems (2001)
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Persistent link: http://EconPapers.repec.org/RePEc:wpa:wuwpem:0108003

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