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

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Specifying smooth transition regression models in the presence of conditional heteroskedasticity of unknown form. / Pavlidis, Efthymios; Paya, I; Peel, D.

In: Studies in Nonlinear Dynamics and Econometrics, Vol. 14, No. 3, 2010, p. 1-38.

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@article{085f66d958a248b48f7011388b31b320,
title = "Specifying smooth transition regression models in the presence of conditional heteroskedasticity of unknown form",
abstract = "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.",
author = "Efthymios Pavlidis and I Paya and D Peel",
year = "2010",
doi = "10.2202/1558-3708.1702",
language = "English",
volume = "14",
pages = "1--38",
journal = "Studies in Nonlinear Dynamics and Econometrics",
issn = "1558-3708",
publisher = "Berkeley Electronic Press",
number = "3",

}

RIS

TY - JOUR

T1 - Specifying smooth transition regression models in the presence of conditional heteroskedasticity of unknown form

AU - Pavlidis, Efthymios

AU - Paya, I

AU - Peel, D

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

U2 - 10.2202/1558-3708.1702

DO - 10.2202/1558-3708.1702

M3 - Journal article

VL - 14

SP - 1

EP - 38

JO - Studies in Nonlinear Dynamics and Econometrics

JF - Studies in Nonlinear Dynamics and Econometrics

SN - 1558-3708

IS - 3

ER -