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Specifying Smooth Transition Regression Models in the Presence of Conditional Heteroskedasticity of Unknown Form

Author

Listed:
  • Pavlidis Efthymios G

    (Lancaster University Management School)

  • Paya Ivan

    (Lancaster University Management School)

  • Peel David A

    (Lancaster University Management School)

Abstract
The specification of Smooth Transition Regression models consists of a sequence of tests, which are typically based on the assumption of i.i.d. errors. In this paper we examine the impact of conditional heteroskedasticity and investigate the performance of several heteroskedasticity robust versions. Simulation evidence indicates that conventional tests can frequently result in finding spurious nonlinearity. Conversely, when the true process is nonlinear in mean, the tests appear to have low size adjusted power and can lead to the selection of misspecified models. The above deficiencies also hold for tests based on Heteroskedasticity Consistent Covariance Matrix Estimators but not for the Fixed Design Wild Bootstrap. We highlight the importance of robust inference through empirical applications.

Suggested Citation

  • Pavlidis Efthymios G & Paya Ivan & Peel David A, 2010. "Specifying Smooth Transition Regression Models in the Presence of Conditional Heteroskedasticity of Unknown Form," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(3), pages 1-40, May.
  • Handle: RePEc:bpj:sndecm:v:14:y:2010:i:3:n:3
    DOI: 10.2202/1558-3708.1702
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    5. Gabriel Zsurkis & JoÃo Nicolau & Paulo M. M. Rodrigues, 2021. "A Re‐Examination of Inflation Persistence Dynamics in OECD Countries: A New Approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(4), pages 935-959, August.
    6. Daiki Maki & Yasushi Ota, 2019. "Testing for time-varying properties under misspecified conditional mean and variance," Papers 1907.12107, arXiv.org, revised Aug 2019.
    7. Afsin Sahin, 2013. "Estimating Money Demand Function by a Smooth Transition Regression Model: An Evidence for Turkey," Working Papers 791, Economic Research Forum, revised Nov 2013.
    8. Pavlidis Efthymios G. & Paya Ivan & Peel David A., 2013. "Nonlinear causality tests and multivariate conditional heteroskedasticity: a simulation study," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(3), pages 297-312, May.
    9. Mohamed Chikhi & Claude Diebolt, 2019. "Testing Nonlinearity through a Logistic Smooth Transition AR Model with Logistic Smooth Transition GARCH Errors," Working Papers of BETA 2019-06, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    10. Rico Belda, Paz, 2013. "No linealidad y asimetría en el proceso generador del Índice Ibex35/Nonlinearity and Asymmetry in the Generator Process of Ibex35 Index," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 31, pages 555-576, Septiembr.
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    12. Daiki Maki & Yasushi Ota, 2021. "Testing for Time-Varying Properties Under Misspecified Conditional Mean and Variance," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1167-1182, April.

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    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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