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Lag Length Estimation in Large Dimensional Systems

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  • Jesus Gonzalo

    (Universidad Carlos III de Madrid)

  • Jean-Yves Pitarakis

    (The University of Reading)

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.
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Suggested Citation

  • Jesus Gonzalo & Jean-Yves Pitarakis, 2001. "Lag Length Estimation in Large Dimensional Systems," Econometrics 0108002, EconWPA.
  • Handle: RePEc:wpa:wuwpem:0108002
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    References listed on IDEAS

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    1. GONZALO, Jesus & PITARAKIS, Jean-Yves, 1994. "Comovements in Large Systems," CORE Discussion Papers 1994065, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Gonzalo, Jesus & Pitarakis, Jean-Yves, 1998. "Specification via model selection in vector error correction models," Economics Letters, Elsevier, vol. 60(3), pages 321-328, September.
    3. Lewis, Richard & Reinsel, Gregory C., 1985. "Prediction of multivariate time series by autoregressive model fitting," Journal of Multivariate Analysis, Elsevier, vol. 16(3), pages 393-411, June.
    4. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 39(3), pages 106-135.
    5. Cheung, Yin-Wong & Lai, Kon S, 1993. "Finite-Sample Sizes of Johansen's Likelihood Ration Tests for Conintegration," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 55(3), pages 313-328, August.
    6. Ho, Mun S & Sorensen, Bent E, 1996. "Finding Cointegration Rank in High Dimensional Systems Using the Johansen Test: An Illustration Using Data Based Monte Carlo Simulations," The Review of Economics and Statistics, MIT Press, vol. 78(4), pages 726-732, November.
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    Citations

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    Cited by:

    1. Ahmad Baharumshah & Evan Lau & Ahmed Khalid, 2006. "Testing Twin Deficits Hypothesis using VARs and Variance Decomposition," Journal of the Asia Pacific Economy, Taylor & Francis Journals, vol. 11(3), pages 331-354.
    2. Audrey Liwan & Evan Lau, 2007. "Managing Growth: The Role of Export, Inflation and Investment in Three ASEAN Neighboring Countries," The IUP Journal of Managerial Economics, IUP Publications, vol. 0(4), pages 7-16, November.
    3. Alain W. HECQ, 2005. "Common Trends and Common Cycles in Latin America: A 2-step vs an Iterative Approach," Computing in Economics and Finance 2005 258, Society for Computational Economics.
    4. E Lau & S Abu Mansor & C-H Puah, 2010. "Revival of the Twin Deficits in Asian Crisis-affected Countries," Economic Issues Journal Articles, Economic Issues, vol. 15(1), pages 29-54, March.
    5. Haldrup, Niels & Hylleberg, Svend & Pons, Gabriel & Sanso, Andreu, 2007. "Common Periodic Correlation Features and the Interaction of Stocks and Flows in Daily Airport Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 21-32, January.
    6. Alfredo García-Hiernaux & José Casals & Miguel Jerez, 2012. "Estimating the system order by subspace methods," Computational Statistics, Springer, vol. 27(3), pages 411-425, September.
    7. Gonzalo, Jesus & Lee, Tae-Hwy, 1998. "Pitfalls in testing for long run relationships," Journal of Econometrics, Elsevier, vol. 86(1), pages 129-154, June.
    8. Gonzalo, Jesus & Pitarakis, Jean-Yves, 1998. "Specification via model selection in vector error correction models," Economics Letters, Elsevier, vol. 60(3), pages 321-328, September.
    9. Dietmar Maringer & Peter Winker, 2004. "Optimal Lag Structure Selection in VEC-Models," Computing in Economics and Finance 2004 155, Society for Computational Economics.
    10. Lau, Evan & Puah, Chin-Hong & Oh, Swee-Ling & Lo, Yan-Ching, 2008. "Causality between White Pepper and Black Pepper: Evidence from Six Markets in Sarawak," MPRA Paper 6552, University Library of Munich, Germany.

    More about this item

    Keywords

    subliminal extant Smith economagic gmm;

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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