Heteroskedasticity in the Presence of Serial Correlation
by Stan Hurn and David McDonald
Recently Bera et al. proposed a test for time-varying variance of
regression errors, known as autoregressive conditional heteroskedasticity
(ARCH), in the presence of serial correlation. We examine the empirical size
and power of this and three other tests. The primary purpose is to provide
information to applied researchers who might wish to choose selectively from
a range of diagnostic tests when ARCH and/or serial correlation are likely to
feature in the data.
size, power, autoregressive conditional heteroskedasticity,
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