Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/67875 
Erscheinungsjahr: 
2009
Schriftenreihe/Nr.: 
Queen's Economics Department Working Paper No. 1218
Verlag: 
Queen's University, Department of Economics, Kingston (Ontario)
Zusammenfassung: 
We propose a semiparametric local polynomial Whittle with noise estimator of the memory parameter in long memory time series perturbed by a noise term which may be serially correlated. The estimator approximates the log-spectrum of the short-memory component of the signal as well as that of the perturbation by two separate polynomials. Including these polynomials we obtain a reduction in the order of magnitude of the bias, but also inflate the asymptotic variance of the long memory estimator by a multiplicative constant. We show that the estimator is consistent for d in (0,1), asymptotically normal for d in (0,3/4), and if the spectral density is sufficiently smooth near frequency zero, the rate of convergence can become arbitrarily close to the parametric rate, sqrt(n). A Monte Carlo study reveals that the proposed estimator performs well in the presence of a serially correlated perturbation term. Furthermore, an empirical investigation of the 30 DJIA stocks shows that this estimator indicates stronger persistence in volatility than the standard local Whittle (with noise) estimator.
Schlagwörter: 
bias reduction
local Whittle
long memory
perturbed fractional process
semiparametric estimation
stochastic volatility
JEL: 
C22
Dokumentart: 
Working Paper

Datei(en):
Datei
Größe
342.11 kB





Publikationen in EconStor sind urheberrechtlich geschützt.