SSE/EFI Working Paper Series in Economics and Finance
No 276:
Testing linearity against smooth transition autoregression using a parametric bootstrap
Joakim Skalin ()
Abstract: When testing the null hypothesis of linearity of a
univariate time series against smooth transition autoregression (STAR),
standard asymptotic distribution results do not apply since nuisance
parameters in the model are unidentified under the null hypothesis. The
prevailing test of Luukkonen, Saikkonen and Teräsvirta (1988) is based on a
linearization, which may adversely affect its power. This paper discusses
an alternative procedure, based on a parametric bootstrap of a likelihood
ratio test statistic, and investigates its size and power properties by a
small simulation study. The results, however, indicate that the power of
the bootstrap test is inferior to that of the existing test.
Keywords: Linearity testing; smooth transition autoregression model; nuisance parameter; nonstandard testing problem; bootstrap test; (follow links to similar papers)
JEL-Codes: C12; C15; C22; (follow links to similar papers)
8 pages, October 28, 1998, Revised December 13, 1998
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