Studies in Nonlinear Dynamics & Econometrics
Ed. by Mizrach, Bruce
IMPACT FACTOR 2018: 0.448
5-years IMPACT FACTOR: 0.877
CiteScore 2018: 0.85
SCImago Journal Rank (SJR) 2018: 0.552
Source Normalized Impact per Paper (SNIP) 2018: 0.561
Mathematical Citation Quotient (MCQ) 2018: 0.07
Specifying Smooth Transition Regression Models in the Presence of Conditional Heteroskedasticity of Unknown Form
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.
Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.