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A class of nearly long-memory time series models

Authors
Journal
International Journal of Forecasting
0169-2070
Publisher
Elsevier
Publication Date
Volume
18
Issue
2
Identifiers
DOI: 10.1016/s0169-2070(01)00157-1
Keywords
  • Hurst Phenomenon
  • Regime Switching
Disciplines
  • Logic

Abstract

Abstract We consider an autoregressive regime-switching model for the dynamic mean structure of a univariate time series. The model allows for a variety of stationary and nonstationary alternatives, and includes the possibility of approximate long-memory behavior. The proposed model includes as special cases white noise, first-order autoregression, and random walk models as well as regime-switching models and the random level-shift model proposed by Chen and Tiao, Journal of Business and Economic Statistics, 8 (1990) p. 83. We describe properties of the model, focusing on its resemblance to long-memory under a certain asymptotic parameterization. We develop a reversible-jump Markov chain Monte Carlo method for Bayesian inference on unknown model parameters and apply the methodology to the Nile River data.

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