A Nonparametric Goodness-of-fit-based Test for Conditional Heteroskedasticity

In this paper we propose a nonparametric test for conditional heteroskedasticity based on a new measure of nonparametric goodness-of-fit (R2). In analogy with the ANOVA tools for classical linear regression models, the nonparametric R2 is obtained for the local polynomial regression of the residuals...

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Main Authors: SU, Liangjun, ULLAH, Aman
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Language:English
Published: Institutional Knowledge at Singapore Management University 2009
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Online Access:https://ink.library.smu.edu.sg/soe_research/1257
https://ink.library.smu.edu.sg/context/soe_research/article/2256/viewcontent/heteroskedasticity09.pdf
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spelling sg-smu-ink.soe_research-22562018-08-30T09:10:48Z A Nonparametric Goodness-of-fit-based Test for Conditional Heteroskedasticity SU, Liangjun ULLAH, Aman In this paper we propose a nonparametric test for conditional heteroskedasticity based on a new measure of nonparametric goodness-of-fit (R2). In analogy with the ANOVA tools for classical linear regression models, the nonparametric R2 is obtained for the local polynomial regression of the residuals from a parametric regression on some covariates. It is close to 0 under the null hypothesis of conditional homoskedasticity and stays away from 0 otherwise. Unlike most popular parametric tests in the literature, the new test does not require the correct specification of parametric conditional heteroskedasticity form and thus is able to detect all kinds of conditional heteroskedasticity of unknown form. We show that after being appropriately centered and standardized, the nonparametric R2 is asymptotically normally distributed under the null hypothesis of conditional homoskedasticity and a sequence of Pitman local alternatives. We also prove the consistency of the test, propose a bootstrap method to obtain the critical values or bootstrap p-values, and justify the validity of the bootstrap method. We conduct a small set of Monte Carlo simulations and compare our test with some popular parametric and nonparametric tests in the literature. Applications to the U.S. real GDP growth rate data indicate that our nonparametric test can reveal certain conditional heteroskedasticity which the parametric tests fail to detect. 2009-10-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/1257 https://ink.library.smu.edu.sg/context/soe_research/article/2256/viewcontent/heteroskedasticity09.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University ANOVA Conditional homoskedasticity Consistency Local polynomial regressions Nonparametric R2 Nonparametric test Econometrics
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic ANOVA
Conditional homoskedasticity
Consistency
Local polynomial regressions
Nonparametric R2
Nonparametric test
Econometrics
spellingShingle ANOVA
Conditional homoskedasticity
Consistency
Local polynomial regressions
Nonparametric R2
Nonparametric test
Econometrics
SU, Liangjun
ULLAH, Aman
A Nonparametric Goodness-of-fit-based Test for Conditional Heteroskedasticity
description In this paper we propose a nonparametric test for conditional heteroskedasticity based on a new measure of nonparametric goodness-of-fit (R2). In analogy with the ANOVA tools for classical linear regression models, the nonparametric R2 is obtained for the local polynomial regression of the residuals from a parametric regression on some covariates. It is close to 0 under the null hypothesis of conditional homoskedasticity and stays away from 0 otherwise. Unlike most popular parametric tests in the literature, the new test does not require the correct specification of parametric conditional heteroskedasticity form and thus is able to detect all kinds of conditional heteroskedasticity of unknown form. We show that after being appropriately centered and standardized, the nonparametric R2 is asymptotically normally distributed under the null hypothesis of conditional homoskedasticity and a sequence of Pitman local alternatives. We also prove the consistency of the test, propose a bootstrap method to obtain the critical values or bootstrap p-values, and justify the validity of the bootstrap method. We conduct a small set of Monte Carlo simulations and compare our test with some popular parametric and nonparametric tests in the literature. Applications to the U.S. real GDP growth rate data indicate that our nonparametric test can reveal certain conditional heteroskedasticity which the parametric tests fail to detect.
format text
author SU, Liangjun
ULLAH, Aman
author_facet SU, Liangjun
ULLAH, Aman
author_sort SU, Liangjun
title A Nonparametric Goodness-of-fit-based Test for Conditional Heteroskedasticity
title_short A Nonparametric Goodness-of-fit-based Test for Conditional Heteroskedasticity
title_full A Nonparametric Goodness-of-fit-based Test for Conditional Heteroskedasticity
title_fullStr A Nonparametric Goodness-of-fit-based Test for Conditional Heteroskedasticity
title_full_unstemmed A Nonparametric Goodness-of-fit-based Test for Conditional Heteroskedasticity
title_sort nonparametric goodness-of-fit-based test for conditional heteroskedasticity
publisher Institutional Knowledge at Singapore Management University
publishDate 2009
url https://ink.library.smu.edu.sg/soe_research/1257
https://ink.library.smu.edu.sg/context/soe_research/article/2256/viewcontent/heteroskedasticity09.pdf
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