Arbeitspapier
Local polynomial Whittle estimation of perturbed fractional processes
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Queen's Economics Department Working Paper ; No. 1218
- Klassifikation
-
Wirtschaft
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- Thema
-
bias reduction
local Whittle
long memory
perturbed fractional process
semiparametric estimation
stochastic volatility
Schätztheorie
Nichtparametrisches Verfahren
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Frederiksen, Per
Nielsen, Frank S.
Nielsen, Morten Ørregaard
- Ereignis
-
Veröffentlichung
- (wer)
-
Queen's University, Department of Economics
- (wo)
-
Kingston (Ontario)
- (wann)
-
2009
- Handle
- Letzte Aktualisierung
-
20.09.2024, 08:22 MESZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Frederiksen, Per
- Nielsen, Frank S.
- Nielsen, Morten Ørregaard
- Queen's University, Department of Economics
Entstanden
- 2009
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