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Testing linearity against smooth transition autoregression using a parametric bootstrap

Listed author(s):
  • Skalin, Joakim


    (Dept for Economic Affairs, Ministry of Finance)

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.

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Paper provided by Stockholm School of Economics in its series SSE/EFI Working Paper Series in Economics and Finance with number 276.

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Length: 8 pages
Date of creation: 28 Oct 1998
Date of revision: 13 Dec 1998
Handle: RePEc:hhs:hastef:0276
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