Testing linearity against smooth transition autoregression using a parametric bootstrap

Joakim Skalin

No 276, SSE/EFI Working Paper Series in Economics and Finance from Stockholm School of Economics

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 (search for similar items in EconPapers)
JEL-codes: C12 C15 C22 (search for similar items in EconPapers)
Pages: 8 pages
Date: 1998-10-28, Revised 1998-12-13
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (19)

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