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On the forecasting ability of ARFIMA models when infrequent breaks occur

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  • Vasco J. Gabriel
  • Luis F. Martins

Abstract

Recent research has focused on the links between long memory and structural breaks, stressing the memory properties that may arise in models with parameter changes. In this paper, we question the implications of this result for forecasting. We contribute to this research by comparing the forecasting abilities of long memory and Markov switching models. Two approaches are employed: the Monte Carlo study and an empirical comparison, using the quarterly Consumer Price inflation rate in Portugal in the period 1968--1998. Although long memory models may capture some in-sample features of the data, we find that their forecasting performance is relatively poor when shifts occur in the series, compared to simple linear and Markov switching models. Copyright Royal Economic Socciety 2004

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Bibliographic Info

Article provided by Royal Economic Society in its journal The Econometrics Journal.

Volume (Year): 7 (2004)
Issue (Month): 2 (December)
Pages: 455-475

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Handle: RePEc:ect:emjrnl:v:7:y:2004:i:2:p:455-475

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Cited by:
  1. Augustine Arize & John Malindretos & Kiseok Nam, 2005. "Inflation and Structural Change in 50 Developing Countries," Atlantic Economic Journal, International Atlantic Economic Society, vol. 33(4), pages 461-471, December.
  2. Vasco Gabriel & Luis Martins, 2010. "Cointegration Tests under Multiple Regime Shifts: An Application to the Stock Price-Dividend Relationship," School of Economics Discussion Papers 0910, School of Economics, University of Surrey.
  3. Bisaglia, Luisa & Gerolimetto, Margherita, 2008. "Forecasting long memory time series when occasional breaks occur," Economics Letters, Elsevier, vol. 98(3), pages 253-258, March.
  4. Rasmus Tangsgaard Varneskov & Pierre Perron, 2011. "Combining Long Memory and Level Shifts in Modeling and Forecasting the Volatility of Asset Returns," CREATES Research Papers 2011-26, School of Economics and Management, University of Aarhus.

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