A nonparametric goodness-of-fit-based test for conditional heteroskedasticity created by Liangjun Su and Aman Ullah

By:

Liangjun Su [author]

Contributor(s):

Aman Ullah [co-author]

Material type: Text

TextSeries: Econometric Theory ; Volume 29, number 1Cambridge: Cambridge University Press, 2013Content type:

text

Media type:

unmediated

Carrier type:

volume

Subject(s):

Nonparametric statistics | -- Statistical test -- Heteroscedasticity

LOC classification:

HB139.T52 ECO

Online resources:

Click here to access online

Abstract: In this paper we propose a new nonparametric test for conditional heteroskedasticity based on a measure of nonparametric goodness-of-fit (R 2 ) that is obtained from the local polynomial regression of the residuals from a parametric regression on some covariates. We show that after being appropriately standardized, the nonparametric R 2 is asymptotically normally distributed under the null hypothesis and a sequence of Pitman local alternatives. We also prove the consistency of the test and propose a bootstrap method to obtain the bootstrap p-values. We conduct a small set of simulations and compare our test with some popular parametric and nonparametric tests in the literature.

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Item type	Current library	Call number	Vol info	Copy number	Status	Notes	Date due	Barcode
Journal Article	Main Library - Special Collections	HB139.T52 ECO (Browse shelf(Opens below))	vol. 29, no. 1 (pages 187-212)	SP17546	Not for loan	For In house Use

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In this paper we propose a new nonparametric test for conditional heteroskedasticity based on a measure of nonparametric goodness-of-fit (R 2 ) that is obtained from the local polynomial regression of the residuals from a parametric regression on some covariates. We show that after being appropriately standardized, the nonparametric R 2 is asymptotically normally distributed under the null hypothesis and a sequence of Pitman local alternatives. We also prove the consistency of the test and propose a bootstrap method to obtain the bootstrap p-values. We conduct a small set of simulations and compare our test with some popular parametric and nonparametric tests in the literature.

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