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

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Author Info
Sandrine Lardic () (University of Paris 10 - MODEM)
Valerie Mignon () (University of Paris 10 - THEMA)

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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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Publisher Info
Article provided by Economics Bulletin in its journal Economics Bulletin.

Volume (Year): 3 (2004)
Issue (Month): 21 ()
Pages: 1-16
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Handle: RePEc:ebl:ecbull:v:3:y:2004:i:21:p:1-16

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Related research
Keywords: ARFIMA processes; bias; exact maximum likelihood estimation; model selection criteria; root mean squared error;

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Find related papers by JEL classification:
C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

  1. Sowell, Fallaw, 1992. "Maximum likelihood estimation of stationary univariate fractionally integrated time series models," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 165-188. [Downloadable!] (restricted)
  2. Doornik, Jurgen A. & Ooms, Marius, 2003. "Computational aspects of maximum likelihood estimation of autoregressive fractionally integrated moving average models," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 333-348, March. [Downloadable!] (restricted)
    Other versions:
  3. Donald W.K. Andrews & Yixiao Sun, 2001. "Local Polynomial Whittle Estimation of Long-range Dependence," Cowles Foundation Discussion Papers 1293, Cowles Foundation, Yale University. [Downloadable!]
  4. Donald W. K. Andrews & Patrik Guggenberger, 2003. "A Bias--Reduced Log--Periodogram Regression Estimator for the Long--Memory Parameter," Econometrica, Econometric Society, vol. 71(2), pages 675-712, March. [Downloadable!] (restricted)
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  5. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July. [Downloadable!] (restricted)
  6. Cheung, Yin-Wong & Diebold, Francis X., 1994. "On maximum likelihood estimation of the differencing parameter of fractionally-integrated noise with unknown mean," Journal of Econometrics, Elsevier, vol. 62(2), pages 301-316, June. [Downloadable!] (restricted)
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  7. Bollerslev, Tim & Jubinski, Dan, 1999. "Equity Trading Volume and Volatility: Latent Information Arrivals and Common Long-Run Dependencies," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 9-21, January.
  8. Shea, Gary S, 1991. "Uncertainty and Implied Variance Bounds in Long-Memory Models of the Interest Rate Term Structure," Empirical Economics, Springer, vol. 16(3), pages 287-312.
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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. [Downloadable!]
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