Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/67757 
Erscheinungsjahr: 
2005
Schriftenreihe/Nr.: 
Queen's Economics Department Working Paper No. 1189
Verlag: 
Queen's University, Department of Economics, Kingston (Ontario)
Zusammenfassung: 
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.
Schlagwörter: 
bias
finite sample distribution
fractional integration
maximum likelihood
Monte Carlo simulation
parametric estimation
semiparametric estimation
wavelet
JEL: 
C14
C15
C22
Dokumentart: 
Working Paper

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