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Finite Sample Comparison Of Parametric, Semiparametric, And Wavelet Estimators Of Fractional Integration

Author

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  • Morten Ø. Nielsen

    (Queen's University and CREATES)

  • Per Houmann Frederiksen

    (Nordea Markets)

Abstract

In this paper we compare through Monte Carlo simulations the finite sample properties of estimators of the fractional differencing parameter, d. This involves frequency domain, time domain, and wavelet based approaches and we consider both parametric and semiparametric estimation methods. The estimators are briefly introduced and compared, and the criteria adopted for measuring finite sample performance are bias and root mean squared error. Most importantly, the simulations reveal that 1) the frequency domain maximum likelihood procedure is superior to the time domain parametric methods, 2) all the estimators are fairly robust to conditionally heteroscedastic errors, 3) the local polynomial Whittle and bias reduced log-periodogram regression estimators are shown to be more robust to short-run dynamics than other semiparametric (frequency domain and wavelet) estimators and in some cases even outperform the time domain parametric methods, and 4) without sufficient trimming of scales the wavelet based estimators are heavily biased.

Suggested Citation

  • Morten Ø. Nielsen & Per Houmann Frederiksen, 2005. "Finite Sample Comparison Of Parametric, Semiparametric, And Wavelet Estimators Of Fractional Integration," Working Paper 1189, Economics Department, Queen's University.
    • Handle: RePEc:qed:wpaper:1189
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    More about this item

    Keywords

    bias; finite sample distribution; fractional integration; maximum likelihood; Monte Carlo simulation; parametric estimation; semiparametric estimation; wavelet;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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