Munich Personal RePEc Archive

# Hausman tests for the error distribution in conditionally heteroskedastic models

Zhu, Ke (2015): Hausman tests for the error distribution in conditionally heteroskedastic models.

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MPRA_paper_66991.pdf

This paper proposes some novel Hausman tests to examine the error distribution in conditionally heteroskedastic models. Unlike the existing tests, all Hausman tests are easy-to-implement with the limiting null distribution of $\chi^{2}$, and moreover, they are consistent and able to detect the local alternative of order n−1=2. The scope of the Hausman test covers all Generalized error distributions and Student’s t distributions. The performance of each Hausman test is assessed by simulated and real data sets.