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Which Lag Length Selection Criteria Should We Employ?

  • Venus Khim-Sen Liew

    ()

    (Universiti Putra Malaysia)

Estimating the lag length of autoregressive process for a time series is a crucial econometric exercise in most economic studies. This study attempts to provide helpfully guidelines regarding the use of lag length selection criteria in determining the autoregressive lag length. The most interesting finding of this study is that Akaike's information criterion (AIC) and final prediction error (FPE) are superior than the other criteria under study in the case of small sample (60 observations and below), in the manners that they minimize the chance of under estimation while maximizing the chance of recovering the true lag length. One immediate econometric implication of this study is that as most economic sample data can seldom be considered “large” in size, AIC and FPE are recommended for the estimation the autoregressive lag length.

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File URL: http://www.accessecon.com/pubs/EB/2004/Volume3/EB-04C20021A.pdf
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Article provided by AccessEcon in its journal Economics Bulletin.

Volume (Year): 3 (2004)
Issue (Month): 33 ()
Pages: 1-9

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Handle: RePEc:ebl:ecbull:eb-04c20021
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  1. Schwert, G William, 1989. "Tests for Unit Roots: A Monte Carlo Investigation," Journal of Business & Economic Statistics, American Statistical Association, vol. 7(2), pages 147-59, April.
  2. Venus Khim-Sen Liew & Terence Tai-Leung Chong & Kian-Ping Lim, 2003. "The inadequacy of linear autoregressive model for real exchange rates: empirical evidence from Asian economies," Applied Economics, Taylor & Francis Journals, vol. 35(12), pages 1387-1392.
  3. Lucio Sarno & Mark P. Taylor, . "Real Exchange Rates under the Recent Float: Unequivocal Evidence of Mean Reversion," Economics and Finance Discussion Papers 97-14, Economics and Finance Section, School of Social Sciences, Brunel University.
  4. Sarantis, Nicholas, 2001. "Nonlinearities, cyclical behaviour and predictability in stock markets: international evidence," International Journal of Forecasting, Elsevier, vol. 17(3), pages 459-482.
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  6. Mansor Ibrhim, 2001. "Financial Factors and the Empirical Behavior of Money Demand: A Case Study of Malaysia," International Economic Journal, Taylor & Francis Journals, vol. 15(3), pages 55-72.
  7. Baharumshah, Ahmad Zubaidi & M. Masih, A. Mansur & Azali, M., 2002. "The stock market and the ringgit exchange rate: a note," Japan and the World Economy, Elsevier, vol. 14(4), pages 471-486, December.
  8. Schwert, G. William, 1987. "Effects of model specification on tests for unit roots in macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 20(1), pages 73-103, July.
  9. Xu, Zhenhui, 2003. "Purchasing power parity, price indices, and exchange rate forcasts," Journal of International Money and Finance, Elsevier, vol. 22(1), pages 105-130, February.
  10. Sarantis, Nicholas, 1999. "Modeling non-linearities in real effective exchange rates," Journal of International Money and Finance, Elsevier, vol. 18(1), pages 27-45, January.
  11. Hirotugu Akaike, 1969. "Fitting autoregressive models for prediction," Annals of the Institute of Statistical Mathematics, Springer, vol. 21(1), pages 243-247, December.
  12. H. Yamada, 2000. "M2 demand relation and effective exchange rate in Japan: a cointegration analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 7(4), pages 229-232.
  13. Taylor, Mark P. & Peel, David A., 2000. "Nonlinear adjustment, long-run equilibrium and exchange rate fundamentals," Journal of International Money and Finance, Elsevier, vol. 19(1), pages 33-53, February.
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