Lag Length Estimation in Large Dimensional Systems
AbstractWe 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.
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Bibliographic InfoPaper provided by EconWPA in its series Econometrics with number 0108003.
Length: 26 pages
Date of creation: 31 Aug 2001
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Note: Type of Document - Adobe pdf; prepared on IBM PC - ; pages: 26
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Dimensionality; Information Criteria; Lag Length Selection; VAR;
Other versions of this item:
- Jesus Gonzalo & Jean-Yves Pitarakis, 2001. "Lag Length Estimation in Large Dimensional Systems," Econometrics 0108002, EconWPA.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
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