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Consistent and Non Degenerate Model Specification Tests Against Smooth Transition and Neural Network Alternatives

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  • Jonathan B. Hill

Abstract

We develop a regression model specification test that directs maximal power toward smooth transition functional forms, and is consistent against any deviation from the null specification. We provide new details regarding whether consistent parametric tests of functional form are asymptotically degenerate: a test of linear autoregression against STAR alternatives is never degenerate. Moreover, a test of Exponential STAR has power attributes entirely associated with the choice of threshold. In a simulation experiment in which all parameters are randomly selected the proposed test has power nearly identical to a most-powerful test for true STAR, neural network and SETAR processes, and dominates popular tests. We apply the test to U.S. output, money, prices and interest rates.

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  • Jonathan B. Hill, 2008. "Consistent and Non Degenerate Model Specification Tests Against Smooth Transition and Neural Network Alternatives," Annals of Economics and Statistics, GENES, issue 90, pages 145-179.
    • Handle: RePEc:adr:anecst:y:2008:i:90:p:145-179
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    Cited by:

    1. Rehim Kılıç, 2016. "Tests for Linearity in Star Models: Supwald and Lm-Type Tests," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(5), pages 660-674, September.
    2. Hill, Jonathan B. & Aguilar, Mike, 2013. "Moment condition tests for heavy tailed time series," Journal of Econometrics, Elsevier, vol. 172(2), pages 255-274.
    3. Hill Jonathan B., 2013. "Stochastically weighted average conditional moment tests of functional form," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(2), pages 121-139, April.

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