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

By: Contributor(s): Material type: TextTextSeries: Econometric Theory ; Volume 29, number 1Cambridge: Cambridge University Press, 2013Content type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
Subject(s): LOC classification:
  • HB139.T52 ECO
Online resources: 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 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

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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