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On the forecasting ability of ARFIMA models when infrequent breaks occur


  • Vasco J. Gabriel
  • Luis F. Martins


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

Suggested Citation

  • Vasco J. Gabriel & Luis F. Martins, 2004. "On the forecasting ability of ARFIMA models when infrequent breaks occur," Econometrics Journal, Royal Economic Society, vol. 7(2), pages 455-475, December.
  • Handle: RePEc:ect:emjrnl:v:7:y:2004:i:2:p:455-475

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    Cited by:

    1. Vasco Gabriel & Luis Martins, 2011. "Cointegration tests under multiple regime shifts: An application to the stock price–dividend relationship," Empirical Economics, Springer, vol. 41(3), pages 639-662, December.
    2. Di Sanzo, Silvestro, 2018. "A Markov switching long memory model of crude oil price return volatility," Energy Economics, Elsevier, vol. 74(C), pages 351-359.
    3. Rodríguez, Gabriel, 2017. "Modeling Latin-American stock and Forex markets volatility: Empirical application of a model with random level shifts and genuine long memory," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 393-420.
    4. José M. Belbute & Alfredo Marvão Pereira, 2016. "Updated Reference Forecasts for Global CO2 Emissions from Fossil-Fuel Consumption," Working Papers 170, Department of Economics, College of William and Mary.
    5. Rasmus T. Varneskov & Pierre Perron, 2018. "Combining long memory and level shifts in modelling and forecasting the volatility of asset returns," Quantitative Finance, Taylor & Francis Journals, vol. 18(3), pages 371-393, March.
    6. Gabriel Rodríguez, 2016. "Modeling Latin-American Stock and Forex Markets Volatility: Empirical Application of a Model with Random Level Shifts and Genuine Long Memory [Modelando la volatilidad de los mercados bursátiles y cam," Documentos de Trabajo / Working Papers 2016-416, Departamento de Economía - Pontificia Universidad Católica del Perú.
    7. Bisaglia, Luisa & Gerolimetto, Margherita, 2008. "Forecasting long memory time series when occasional breaks occur," Economics Letters, Elsevier, vol. 98(3), pages 253-258, March.
    8. Augustine Arize & John Malindretos & Kiseok Nam, 2005. "Inflation and Structural Change in 50 Developing Countries," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 33(4), pages 461-471, December.

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