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On the estimation of short memory components in long memory time series models

Author

Listed:
  • Baillie Richard T.
  • Kapetanios George

    (School of Economics and Finance, Queen Mary University of London, UK)

Abstract

A substantial amount of recent time series research has emphasized semi-parameteric estimators of a long memory parameter and we provide a selective review of the literature on this issue. We consider such estimators applied to the issue of estimating the parameters relating to a short memory process which is embedded within the long memory process. We consider the fractional differencing filter and the subsequent properties of a two step estimator of the short memory parameters. We conclude that while the semi-parametric estimators can have excellent properties in terms of estimating the long memory parameter, they do not have good properties when applied to the two step estimator of short memory I(0) parameters. In particular, these estimators compare poorly in terms of bias and mean squared error (MSE) with the systems based maximum likelihood estimator (MLE).

Suggested Citation

  • Baillie Richard T. & Kapetanios George, 2016. "On the estimation of short memory components in long memory time series models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(4), pages 365-375, September.
    • Handle: RePEc:bpj:sndecm:v:20:y:2016:i:4:p:365-375:n:8
    DOI: 10.1515/snde-2015-0120
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    References listed on IDEAS

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    More about this item

    Keywords

    long memory; nonlinear; time series;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General

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