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The exact minimum likelihood estimation of ARFIMA processes and model selection criteria: A Monte Carlo study

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  • S. Lardic
  • V. Mignon

Abstract

We propose a detailed Monte Carlo study of model selection criteria when the exact maximum likelihood (EML) method is used to estimate ARFIMA processes. More specifically, our object is to assess the performance of two automatic selection criteria in the presence of long-term memory: Akaike and Schwarz information criteria. Two special processes are considered: a pure fractional noise model (ARFIMA(0,d,0)) and an ARFIMA(1,d,0) process. For each criterion, we compute bias and root mean squared error for various d and AR(1) parameter values. Obtained results suggest that the Schwarz information criterion frequently selects the right model. Moreover, this criterion outperforms the other one in terms of bias and RMSE, for both pure fractional noise and ARFIMA processes.
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  • S. Lardic & V. Mignon, 2003. "The exact minimum likelihood estimation of ARFIMA processes and model selection criteria: A Monte Carlo study," THEMA Working Papers 2003-06, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    • Handle: RePEc:ema:worpap:2003-06
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    1. S. Lardic & V. Mignon & F. Murtin, 2003. "Frequency-domain estimation of fractionally integrated processes: impact of short-term components on the bandwidth," THEMA Working Papers 2003-08, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    2. M.Y. TEWELDEMEDHIN & H.D. VAN SCHALKWYK & Rena RAVINDER, 2009. "The Agricultural Industry And Economic Growth In South Africa – An Empirical Analysis," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 4, pages 43-56, November.
    3. Kathleen Walsh & David Tan, 2008. "Monetary Policy Surprises and the Bank Bill Term Premium," Australian Journal of Management, Australian School of Business, vol. 33(2), pages 231-260, December.

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    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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