Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/67875
Authors: 
Frederiksen, Per
Nielsen, Frank S.
Nielsen, Morten Ørregaard
Year of Publication: 
2009
Series/Report no.: 
Queen's Economics Department Working Paper 1218
Abstract: 
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.
Subjects: 
bias reduction
local Whittle
long memory
perturbed fractional process
semiparametric estimation
stochastic volatility
JEL: 
C22
Document Type: 
Working Paper

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