English, Article, Journal or magazine article edition: Specifying Smooth Transition Regression Models in the Presence of Conditional Heteroskedasticity of Unknown Form David Peel; Ivan Paya; E Pavlidis

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Bookmark: http://trove.nla.gov.au/version/59313
Physical Description
  • preprint
Language
  • English

Edition details

Title
  • Specifying Smooth Transition Regression Models in the Presence of Conditional Heteroskedasticity of Unknown Form
Author
  • David Peel
  • Ivan Paya
  • E Pavlidis
Physical Description
  • preprint
Notes
  • 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.
  • Time Series, Robust Linearity Test, Heteroskedasticity Consistent Covariance Matrix Estimator, Wild Bootstrap, Monte Carlo Simulation
  • RePEc:lan:wpaper:005913
Language
  • English
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