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Testing for threshold effect in ARFIMA models: Application to US unemployment rate data

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  • Amine LAHIANI

    (ESC-Rennes School of Business and EconomiX, University of Paris 10 Nanterre)

  • Olivier SCAILLET

    (Université de Genève HEC and Swiss Finance Institute)

Abstract

Macroeconomic time series often involve a threshold effect in their ARMA representation, and exhibit long memory features. In this paper we introduce a new class of threshold ARFIMA models to account for this. The threshold effect is introduced in the autoregressive and/or the fractional integration parameters, and can be tested for using LM tests. Monte Carlo experiments show the desirable finite sample size and power of the test with an exact maximum likelihood estimator of the long memory parameter. Simulations also show that a model selection strategy is available to discriminate between the competing threshold ARFIMA models. The methodology is applied to US unemployment rate data where we find a significant threshold effect in the ARFIMA representation and a better forecasting performance relative to TAR and symmetric ARFIMA models.

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Bibliographic Info

Paper provided by Swiss Finance Institute in its series Swiss Finance Institute Research Paper Series with number 08-42.

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Length: 21 pages
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Handle: RePEc:chf:rpseri:rp0842

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Web page: http://www.SwissFinanceInstitute.ch
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Keywords: Threshold ARFIMA; LM test; Asymmetric time series;

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References

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Cited by:
  1. Aloy Marcel & Tong Charles Lai & Peguin-Feissolle Anne & Dufrénot Gilles, 2013. "A smooth transition long-memory model," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(3), pages 281-296, May.
  2. Arouri, Mohamed El Hedi & Hammoudeh, Shawkat & Lahiani, Amine & Nguyen, Duc Khuong, 2012. "Long memory and structural breaks in modeling the return and volatility dynamics of precious metals," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(2), pages 207-218.

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