Article thumbnail
Location of Repository

Effects of temporal aggregation on estimates and forecasts of fractionally integrated processes : a Monte Carlo study

By L. R. Souza and Jeremy Smith

Abstract

For a fractionally integrated ARFIMA(p,d,q) model, temporal aggregation changes the order of the process to an ARFIMA(p,d,infinity), while leaving the value of d unchanged. This paper analyses the effects of temporal aggregation on the estimated long memory parameter, d, using both semi-parametric and parametric estimation methods. We find that if, for the non-aggregated series, the bias in the fractional parameter is large due to the influence of short run AR and MA parameters, temporal aggregation can reduce this bias. We compare aggregated forecasts from the underlying (non-aggregated) series with forecasts from the aggregated series and find that for d < 0, forecasts from the aggregated series are generally superior. For d > 0, the forecast comparison results are less clear-cut. (C) 2003 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved

Topics: HC, HD28
Publisher: Elsevier
Year: 2004
DOI identifier: 10.1016/s0169-2070(03)00066-9
OAI identifier: oai:wrap.warwick.ac.uk:8101
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://dx.doi.org/10.1016/S016... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.