Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/67757
Authors: 
Nielsen, Morten Ørregaard
Frederiksen, Per
Year of Publication: 
2005
Series/Report no.: 
Queen's Economics Department Working Paper 1189
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.
Subjects: 
bias
finite sample distribution
fractional integration
maximum likelihood
Monte Carlo simulation
parametric estimation
semiparametric estimation
wavelet
JEL: 
C14
C15
C22
Document Type: 
Working Paper

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