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Penalised Maximum Likelihood Estimation for Fractional Guassian Processes

Author

Listed:
  • Offer Lieberman

    (Technion-Israel Institute of Technology)

Abstract

We apply and extend Firth's (1993) modified score estimator to deal with a class of stationary Gaussian long-memory processes. Our estimator removes the first order bias of the maximum likelihood estimator. A small simulation study reveals the reduction in the bias is considerable, while it does not inflate the corresponding mean squared error.

Suggested Citation

  • Offer Lieberman, 2001. "Penalised Maximum Likelihood Estimation for Fractional Guassian Processes," Cowles Foundation Discussion Papers 1348, Cowles Foundation for Research in Economics, Yale University.
    • Handle: RePEc:cwl:cwldpp:1348
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    File URL: https://cowles.yale.edu/sites/default/files/files/pub/d13/d1348.pdf
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    Citations

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    Cited by:

    1. Stelios Arvanitis & Antonis Demos, 2015. "A class of indirect inference estimators: higher‐order asymptotics and approximate bias correction," Econometrics Journal, Royal Economic Society, vol. 18(2), pages 200-241, June.
    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.

    More about this item

    Keywords

    ARFIMA; Firth's formula; fractional differencing; approximate modification;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

    Statistics

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