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Lag Length Estimation in Large Dimensional Systems

Author

Listed:
  • 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.

Suggested Citation

  • Jesus Gonzalo & Jean-Yves Pitarakis, 2001. "Lag Length Estimation in Large Dimensional Systems," Econometrics 0108003, University Library of Munich, Germany.
    • Handle: RePEc:wpa:wuwpem:0108003
    Note: Type of Document - Adobe pdf; prepared on IBM PC - ; pages: 26
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    References listed on IDEAS

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    4. 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.
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    2. 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.
    3. 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.
    4. 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.
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    7. Pitarakis, Jean-Yves, 2014. "A joint test for structural stability and a unit root in autoregressions," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 577-587.
    8. Ming-Liang Yeh & Hsiao-Ping Chu & Peter Sher & Yi-Chia Chiu, 2010. "R&D intensity, firm performance and the identification of the threshold: fresh evidence from the panel threshold regression model," Applied Economics, Taylor & Francis Journals, vol. 42(3), pages 389-401.
    9. Gonzalo, Jesus & Lee, Tae-Hwy, 1998. "Pitfalls in testing for long run relationships," Journal of Econometrics, Elsevier, vol. 86(1), pages 129-154, June.
    10. Dobromił Serwa & Piotr Wdowiński, 2017. "Modeling Macro-Financial Linkages: Combined Impulse Response Functions in SVAR Models," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 9(4), pages 323-357, December.
    11. 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.
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    13. 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.
    14. Yaser Abolghasemi & Stanko Dimitrov, 2021. "Determining the causality between U.S. presidential prediction markets and global financial markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4534-4556, July.
    15. Peter Winker & Dietmar Maringer, 2004. "Optimal Lag Structure Selection in VEC-Models," Contributions to Economic Analysis, in: New Directions in Macromodelling, pages 213-234, Emerald Group Publishing Limited.
    16. Stephan B. Bruns & David I. Stern, 2015. "Meta-Granger causality testing," CAMA Working Papers 2015-22, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    17. Chaban, Maxym, 2011. "Home bias, distribution services and determinants of real exchange rates," Journal of Macroeconomics, Elsevier, vol. 33(4), pages 793-806.
    18. 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.
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    20. John D. Levendis, 2018. "Time Series Econometrics," Springer Texts in Business and Economics, Springer, number 978-3-319-98282-3, December.

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    More about this item

    Keywords

    Dimensionality; Information Criteria; Lag Length Selection; VAR;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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