Estimation of seasonal fractionally integrated processes

Authors:
Valderio Anselmo Reisen;Alexandre L. Rodrigues;Wilfredo Palma
Affiliations:
Department of Statistics, Universidade Federal de Espirito Santo, R. Fernando Ferrari s/n, Vitoria ES 29060900, Brazil;Department of Statistics, Universidade Federal de Espirito Santo, R. Fernando Ferrari s/n, Vitoria ES 29060900, Brazil;Department of Statistics, Pontificia Universidad Católica de Chile, Santiago, Chile
Venue:
Computational Statistics & Data Analysis
Year:
2006

Citing 1
Cited 4

Asymptotic theory of statistical inference

Asymptotic theory of statistical inference

Generalised long-memory GARCH models for intra-daily volatility

Computational Statistics & Data Analysis
Local bootstrap approaches for fractional differential parameter estimation in ARFIMA models

Computational Statistics & Data Analysis
Modeling and forecasting daily average PM10 concentrations by a seasonal long-memory model with volatility

Environmental Modelling & Software
Original Articles: A semiparametric approach to estimate two seasonal fractional parameters in the SARFIMA model

Mathematics and Computers in Simulation

Quantified Score

Hi-index	0.03

Visualization

Abstract

This paper discusses the estimation of fractionally integrated processes with seasonal components. In order to estimate the fractional parameters, we propose several estimators obtained from the regression of the log-periodogram on different bandwidths selected around and/or between the seasonal frequencies. For comparison purposes, the semi-parametric method introduced in Geweke and Porter-Hudak (1983) and Porter-Hudak (1990) and the maximum-likelihood estimates (ML) are also considered. As indicated by the Monte Carlo simulations, the performance of the estimators proposed is good even for small sample sizes.