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Simulation-based Finite Sample Linearity Test against Smooth Transition Models

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  • Andrés González
  • Timo Teräsvirta

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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Bibliographic 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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  1. 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, Elsevier, vol. 28(3), pages 531-553, December.
  2. Andrews, Donald W K & Ploberger, Werner, 1994. "Optimal Tests When a Nuisance Parameter Is Present Only under the Alternative," Econometrica, Econometric Society, Econometric Society, vol. 62(6), pages 1383-1414, November.
  3. Hansen, Bruce E, 1996. "Inference When a Nuisance Parameter Is Not Identified under the Null Hypothesis," Econometrica, Econometric Society, Econometric Society, vol. 64(2), pages 413-30, March.
  4. DUFOUR, Jean-Marie & KHALAF, Lynda & BERNARD, Jean-Thomas, 2001. "Simulation-Based Finite-Sample Tests for Heteroskedasticity and ARCH Effects," Cahiers de recherche, Universite de Montreal, Departement de sciences economiques 2001-08, Universite de Montreal, Departement de sciences economiques.
  5. Dufour, Jean-Marie & Kiviet, Jan F., 1996. "Exact tests for structural change in first-order dynamic models," Journal of Econometrics, Elsevier, Elsevier, vol. 70(1), pages 39-68, January.
  6. Andres Gonzalez & Timo Terasvirta & Dick van Dijk, 2005. "Panel Smooth Transition Regression Models," Research Paper Series, Quantitative Finance Research Centre, University of Technology, Sydney 165, Quantitative Finance Research Centre, University of Technology, Sydney.
  7. Goffe, William L. & Ferrier, Gary D. & Rogers, John, 1994. "Global optimization of statistical functions with simulated annealing," Journal of Econometrics, Elsevier, Elsevier, vol. 60(1-2), pages 65-99.
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Cited by:
  1. Andrea Fracasso & Giuseppe Vittucci Marzetti, 2014. "International R&D Spillovers, Absorptive Capacity and Relative Backwardness: A Panel Smooth Transition Regression Model," International Economic Journal, Taylor & Francis Journals, Taylor & Francis Journals, vol. 28(1), pages 137-160, March.
  2. E Pavlidis & I Paya & D Peel, 2009. "Specifying Smooth Transition Regression Models in the Presence of Conditional Heteroskedasticity of Unknown Form," Working Papers 599040, Lancaster University Management School, Economics Department.
  3. Giulio Cainelli & Andrea Fracasso & Giuseppe Vittucci Marzetti, 2012. "Spatial agglomeration and productivity in Italy: a panel smooth transition regression approach," Openloc Working Papers, Public policies and local development 1204, Public policies and local development.

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