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

Author

Listed:
  • 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.

Suggested Citation

  • Amine LAHIANI & Olivier SCAILLET, 2008. "Testing for threshold effect in ARFIMA models: Application to US unemployment rate data," Swiss Finance Institute Research Paper Series 08-42, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp0842
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    2. Aikins Abakah, Emmanuel Joel & Gil-Alana, Luis A. & Tripathy, Trilochan, 2022. "Stochastic structure of metal prices: Evidence from fractional integration non-linearities and breaks," Resources Policy, Elsevier, vol. 78(C).
    3. 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.
    4. Malinda & Maya & Jo-Hui & Chen, 2022. "Testing for the Long Memory and Multiple Structural Breaks in Consumer ETFs," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 12(6), pages 1-6.
    5. Aloy Marcel & Dufrénot Gilles & Tong Charles Lai & Peguin-Feissolle Anne, 2013. "A smooth transition long-memory model," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(3), pages 281-296, May.
    6. Guglielmo Maria Caporale & Luis A. Gil-Alana & Pablo Vicente Trejo, 2021. "Unemployment Persistence in Europe: Evidence from the 27 EU Countries," CESifo Working Paper Series 9392, CESifo.
    7. Caporale, Guglielmo Maria & Gil-Alana, Luis A. & Poza, Carlos, 2020. "High and low prices and the range in the European stock markets: A long-memory approach," Research in International Business and Finance, Elsevier, vol. 52(C).
    8. Boubaker Heni & Canarella Giorgio & Gupta Rangan & Miller Stephen M., 2021. "Long-memory modeling and forecasting: evidence from the U.S. historical series of inflation," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 25(5), pages 289-310, December.
    9. Monge, Manuel, 2021. "U.S. historical initial jobless claims. Is it different with the coronavirus crisis? A fractional integration analysis," International Economics, Elsevier, vol. 167(C), pages 88-95.
    10. Donya Rahmani & Damien Fay, 2022. "A state‐dependent linear recurrent formula with application to time series with structural breaks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 43-63, January.

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    Keywords

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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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