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Simulation-based Finite Sample Linearity Test against Smooth Transition Models

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Author Info
Andrés González
Timo Teräsvirta

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Abstract

In this paper, we use Monte Carlo (MC) testing techniques for testing linearity against smooth transition models. The MC approach allows us to introduce a new test that differs in two respects from the tests existing in the literature. First, the test is exact in the sense that the probability of rejecting the null when it is true is always less than or equal to the nominal size of the test. Secondly, the test is not based on an auxiliary regression obtained by replacing the model under the alternative by approximations based on a Taylor expansion. We also apply MC testing methods for size correcting the test proposed by Luukkonen, Saikkonen and Teräsvirta ("Biometrika", Vol. 75, 1988, p. 491). The results show that the power loss implied by the auxiliary regression-based test is non-existent compared with a supremum-based test but is more substantial when compared with the three other tests under consideration. Copyright 2006 Blackwell Publishing Ltd.

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File URL: http://www.blackwell-synergy.com/doi/abs/10.1111/j.1468-0084.2006.00457.x
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Publisher Info
Article provided by Department of Economics, University of Oxford in its journal Oxford Bulletin of Economics and Statistics.

Volume (Year): 68 (2006)
Issue (Month): s1 (December)
Pages: 797-812
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Handle: RePEc:bla:obuest:v:68:y:2006:i:s1:p:797-812

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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Andrews, Donald W K & Ploberger, Werner, 1994. "Optimal Tests When a Nuisance Parameter Is Present Only under the Alternative," Econometrica, Econometric Society, vol. 62(6), pages 1383-1414, November. [Downloadable!] (restricted)
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  2. Khalaf, Lynda & Saphores, Jean-Daniel & Bilodeau, Jean-Francois, 2003. "Simulation-based exact jump tests in models with conditional heteroskedasticity," Journal of Economic Dynamics and Control, Elsevier, vol. 28(3), pages 531-553, December. [Downloadable!] (restricted)
  3. Hansen, B.E., 1991. "Inference when a Nuisance Parameter is Not Identified Under the Null Hypothesis," RCER Working Papers 296, University of Rochester - Center for Economic Research (RCER).
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  4. Goffe, William L. & Ferrier, Gary D. & Rogers, John, 1994. "Global optimization of statistical functions with simulated annealing," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 65-99. [Downloadable!] (restricted)
  5. González, Andrés & Teräsvirta, Timo & van Dijk, Dick, 2005. "Panel Smooth Transition Regression Models," Working Paper Series in Economics and Finance 604, Stockholm School of Economics. [Downloadable!]
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  6. Dr. Peter Kenning & Hilke Plassmann, 2004. "NeuroEconomics," Experimental 0412005, EconWPA. [Downloadable!]
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. David Peel & Ivan Paya & E Pavlidis, 2009. "Specifying Smooth Transition Regression Models in the Presence of Conditional Heteroskedasticity of Unknown Form," Working Papers 005913, Lancaster University Management School, Economics Department. [Downloadable!]
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