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.

Language
Englisch

Bibliographic citation
Series: Queen's Economics Department Working Paper ; No. 1218

Classification
Wirtschaft
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Subject
bias reduction
local Whittle
long memory
perturbed fractional process
semiparametric estimation
stochastic volatility
Schätztheorie
Nichtparametrisches Verfahren

Event
Geistige Schöpfung
(who)
Frederiksen, Per
Nielsen, Frank S.
Nielsen, Morten Ørregaard
Event
Veröffentlichung
(who)
Queen's University, Department of Economics
(where)
Kingston (Ontario)
(when)
2009

Handle
Last update
20.09.2024, 8:22 AM CEST

Data provider

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Object type

  • Arbeitspapier

Associated

  • Frederiksen, Per
  • Nielsen, Frank S.
  • Nielsen, Morten Ørregaard
  • Queen's University, Department of Economics

Time of origin

  • 2009

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