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

Pavlidis, E and Paya, I and Peel, D (2009) Specifying Smooth Transition Regression Models in the Presence of Conditional Heteroskedasticity of Unknown Form. Working Paper. The Department of Economics, Lancaster University.

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    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.

    Item Type: Monograph (Working Paper)
    Uncontrolled Keywords: Time Series ; Robust Linearity Test ; Heteroskedasticity Consistent Covariance Matrix Estimator ; Wild Bootstrap ; Monte Carlo Simulation
    Subjects: UNSPECIFIED
    Departments: Lancaster University Management School > Economics
    ID Code: 48952
    Deposited By: ep_importer_pure
    Deposited On: 11 Jul 2011 22:24
    Refereed?: No
    Published?: Published
    Last Modified: 17 Sep 2013 09:51
    Identification Number:
    URI: http://eprints.lancs.ac.uk/id/eprint/48952

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