Lag length estimation in large dimensional systems

Gonzalo, Jesus and Pitarakis, Jean-Yves (2002) Lag length estimation in large dimensional systems Journal of Time Series Analysis, 23, (4), pp. 401-423. (doi:10.1111/1467-9892.00270).


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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.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1111/1467-9892.00270
Keywords: dimensionality, information criteria, lag length selection, VAR
ePrint ID: 33367
Date :
Date Event
Date Deposited: 16 May 2006
Last Modified: 16 Apr 2017 22:16
Further Information:Google Scholar

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