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Specifying smooth transition regression models in the presence of conditional heteroskedasticity of unknown form

Research output: Contribution to journalJournal articlepeer-review

<mark>Journal publication date</mark>2010
<mark>Journal</mark>Studies in Nonlinear Dynamics and Econometrics
Issue number3
Number of pages38
Pages (from-to)1-38
Publication StatusPublished
<mark>Original language</mark>English


he 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.