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Lag length estimation in large dimensional systems

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  • JESÚS GONZALO
  • JEAN‐YVES PITARAKIS

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 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.

Suggested Citation

  • Jesús Gonzalo & Jean‐Yves Pitarakis, 2002. "Lag length estimation in large dimensional systems," Journal of Time Series Analysis, Wiley Blackwell, vol. 23(4), pages 401-423, July.
    • Handle: RePEc:bla:jtsera:v:23:y:2002:i:4:p:401-423
    DOI: 10.1111/1467-9892.00270
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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. Gonzalo, Jesús & Pitarakis, Jean-Yves, 2021. "Spurious relationships in high-dimensional systems with strong or mild persistence," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1480-1497.
    3. Stephan B. Bruns & Christian Gross & David I. Stern, 2014. "Is There Really Granger Causality between Energy Use and Output?," The Energy Journal, , vol. 35(4), pages 101-134, October.
    4. 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.
    5. Liwan, Audrey & Lau, Evan, 2007. "Managing Growth: The Role of Export, Inflation and Investment in three ASEAN Neighboring Countries," MPRA Paper 3952, University Library of Munich, Germany.
    6. 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.
    7. 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.
    8. Chaban, Maxym, 2011. "Home bias, distribution services and determinants of real exchange rates," Journal of Macroeconomics, Elsevier, vol. 33(4), pages 793-806.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. John D. Levendis, 2018. "Time Series Econometrics," Springer Texts in Business and Economics, Springer, number 978-3-319-98282-3, March.
    16. Efstathios Polyzos & Costas Siriopoulos, 2024. "Autoregressive Random Forests: Machine Learning and Lag Selection for Financial Research," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 225-262, July.
    17. 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.
    18. 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.
    19. 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.
    20. Gonzalo, Jesus & Lee, Tae-Hwy, 1998. "Pitfalls in testing for long run relationships," Journal of Econometrics, Elsevier, vol. 86(1), pages 129-154, June.

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    JEL classification:

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

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