Autoregressive Conditional
Heteroskedasticity in the Presence of Serial Correlation
by Stan Hurn and David McDonald
.
Abstract:
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.
Key Words:
size, power, autoregressive conditional heteroskedasticity,
serial correlation
Authors:
Stan Hurn,
shurn@cupid.ecom.unimelb.edu.au
David McDonald,
david.mcdonald@ml.csiro.au
Editor:
Don Edwards
,
edwards@math.scarolina.edu
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