ESTIMATION OF PARAMETERS IN ARFIMA PROCESSES: A SIMULATION STUDY

Valdério Anselmo Reisen · Bovas Abraham · Sílvia R. C. Lopes

63 citations0/0 files storedCommunications in Statistics - Simulation and Computation

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Complex Systems and Time Series Analysis Time Series Analysis and Forecasting Statistical and numerical algorithms

Abstract

It is known that, in the presence of short memory components, the estimation of the fractional parameter d in an Autoregressive Fractionally Integrated Moving Average, ARFIMA(p, d, q), process has some difficulties (see [1]). In this paper, we continue the efforts made by Smith et al. [1] and Beveridge and Oickle [2] by conducting a simulation study to evaluate the convergence properties of the iterative estimation procedure suggested by Hosking [3]. In this context we consider some semiparametric approaches and a parametric method proposed by Fox-Taqqu[4]. We also investigate the method proposed by Robinson [5] and a modification using the smoothed periodogram function.

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1977Time Series Analysis: Forecasting and Control 1983THE ESTIMATION AND APPLICATION OF LONG MEMORY TIME SERIES MODELS 1996Statistics for Long-Memory Processes.1995Gaussian Semiparametric Estimation of Long Range Dependence 1995Log-Periodogram Regression of Time Series with Long Range Dependence 1995ESTIMATORS FOR LONG-RANGE DEPENDENCE: AN EMPIRICAL STUDY 1992Maximum likelihood estimation of stationary univariate fractionally integrated time series models 1998The mean squared error of Geweke and Porter‐Hudak's estimator of the memory parameter of a long‐memory time series

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