Template-Type: ReDIF-Paper 1.0
Author-Name: E Pavlidis
Author-Name-First: E
Author-Name-Last: Pavlidis
Author-Name: I Paya
Author-Name-First: I
Author-Name-Last: Paya
Author-Name: D Peel
Author-Name-First: D
Author-Name-Last: Peel
Title: Specifying Smooth Transition Regression
Models in the Presence of Conditional
Heteroskedasticity of Unknown Form
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.
Creation-Date: 2009
File-URL: http://www.lancaster.ac.uk/media/lancaster-university/content-assets/documents/lums/economics/working-papers/SmoothTransitionRegression.pdf
File-Format: application/pdf
Number: 599040
Classification-JEL: 
Keywords: Time Series, Robust Linearity Test, Heteroskedasticity Consistent Covariance Matrix Estimator, WildBootstrap, Monte Carlo Simulation
Handle: RePEc:lan:wpaper:599040