Finite Sample Comparison of Parametric, Semiparametric, and Wavelet Estimators of Fractional Integration

By Morten Ørregaard Nielsen, Per Frederiksen. 2005

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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.

Subfields: Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/74efd9e00dbd613ee3a0455c191b4e26543fb441

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Citations in the corpus

LOCAL WHITTLE ESTIMATION OF FRACTIONAL INTEGRATION FOR NONLINEAR PROCESSES (2007, methodology): “For example, the normalized partial sum of X, when d> 4 converges to two distinct fractional Brownian motions (Marinucci and Robinson, 1999a, 1999b). A detailed discussion of their differences can be found in Robinson (2005) and Shimotsu and Phillips (SP hereafter) (2006).”