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

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  • Lahiani, A.
  • Scaillet, O.

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 fractional integration parameters, and can be tested for using LM tests. Monte Carlo experiments show the desirable finite sample size and the 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

Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 25 (2009)
Issue (Month): 2 ()
Pages: 418-428

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Handle: RePEc:eee:intfor:v:25:y:2009:i:2:p:418-428

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Web page: http://www.elsevier.com/locate/ijforecast

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Keywords: Threshold ARFIMA LM test Asymmetric time series;

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References

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  1. Lo, Andrew W, 1991. "Long-Term Memory in Stock Market Prices," Econometrica, Econometric Society, vol. 59(5), pages 1279-313, September.
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  8. Francis X. Diebold & Atsushi Inoue, 2000. "Long Memory and Regime Switching," NBER Technical Working Papers 0264, National Bureau of Economic Research, Inc.
  9. Philip Rothman, . "Forecasting Asymmetric Unemployment Rates," Working Papers 9618, East Carolina University, Department of Economics.
  10. van Dijk, Dick & Franses, Philip Hans & Paap, Richard, 2002. "A nonlinear long memory model, with an application to US unemployment," Journal of Econometrics, Elsevier, vol. 110(2), pages 135-165, October.
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  12. Sowell, Fallaw, 1992. "Maximum likelihood estimation of stationary univariate fractionally integrated time series models," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 165-188.
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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. Mohamed El Hedi Arouri & Shawkat Hammoudeh & Amine Lahiani & Duc Khuong Nguyen, 2013. "Long memory and structural breaks in modeling the return and volatility dynamics of precious metals," Working Papers hal-00798033, HAL.

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