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

Listed author(s):
  • Pavlidis Efthymios G

    ()

    (Lancaster University Management School)

  • Paya Ivan

    ()

    (Lancaster University Management School)

  • Peel David A

    ()

    (Lancaster University Management School)

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.

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Article provided by De Gruyter in its journal Studies in Nonlinear Dynamics & Econometrics.

Volume (Year): 14 (2010)
Issue (Month): 3 (May)
Pages: 1-40

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Handle: RePEc:bpj:sndecm:v:14:y:2010:i:3:n:3
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