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Asymptotic Theory for Regressions with Smoothly Changing Parameters

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  • Eric Hillebrand

    ()
    (Aarhus University and CREATES)

  • Marcelo C. Medeiros

    ()
    (PONTIFICAL CATHOLIC UNIVERSITY OF RIO DE JANEIRO)

  • Junyue Xu

    ()
    (LOUISIANA STATE UNIVERSITY)

Abstract

We derive asymptotic properties of the quasi maximum likelihood estimator of smooth transition regressions when time is the transition variable. The consistency of the estimator and its asymptotic distribution are examined. It is shown that the estimator converges at the usual square-root-of-T rate and has an asymptotically normal distribution. Finite sample properties of the estimator are explored in simulations. We illustrate with an application to US inflation and output data.

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Bibliographic Info

Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2012-31.

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Length: 31
Date of creation: 12 Jun 2012
Date of revision:
Handle: RePEc:aah:create:2012-31

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Web page: http://www.econ.au.dk/afn/

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Keywords: Regime switching; smooth transition regression; asymptotic theory.;

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  1. Areosa, Waldyr Dutra & McAleer, Michael & Medeiros, Marcelo C., 2011. "Moment-based estimation of smooth transition regression models with endogenous variables," Journal of Econometrics, Elsevier, vol. 165(1), pages 100-111.
  2. Donald W.K. Andrews, 1990. "Generic Uniform Convergence," Cowles Foundation Discussion Papers 940, Cowles Foundation for Research in Economics, Yale University.
  3. Saikkonen, Pentti & Choi, In, 2004. "Cointegrating Smooth Transition Regressions," Econometric Theory, Cambridge University Press, vol. 20(02), pages 301-340, April.
  4. Medeiros, Marcelo & Veiga, Alvaro, 2000. "Diagnostic Checking in a Flexible Nonlinear Time Series Model," Working Paper Series in Economics and Finance 386, Stockholm School of Economics, revised 15 Jan 2001.
  5. Strikholm, Birgit, 2006. "Determining the number of breaks in a piecewise linear regression model," Working Paper Series in Economics and Finance 648, Stockholm School of Economics.
  6. Shiqing Ling & Michael McAleer, 2001. "Asymptotic Theory for a Vector ARMA-GARCH Model," ISER Discussion Paper 0549, Institute of Social and Economic Research, Osaka University.
  7. Mayte Suarez -Farinas & Carlos E. Pedreira & Marcelo C. Medeiros, 2004. "Local Global Neural Networks: A New Approach for Nonlinear Time Series Modeling," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 1092-1107, December.
  8. Li, W K & Ling, Shiqing & McAleer, Michael, 2002. " Recent Theoretical Results for Time Series Models with GARCH Errors," Journal of Economic Surveys, Wiley Blackwell, vol. 16(3), pages 245-69, July.
  9. Lin, Chien-Fu Jeff & Terasvirta, Timo, 1994. "Testing the constancy of regression parameters against continuous structural change," Journal of Econometrics, Elsevier, vol. 62(2), pages 211-228, June.
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Cited by:
  1. Gilles Dufrénot & Anwar Khayat, 2014. "Monetary Policy Switching in the Euro Area and Multiple Equilibria: An Empirical Investigation," Working Papers halshs-00973504, HAL.
  2. A S Hurn & Annastiina Silvennoinen & Timo Terasvirta, 2014. "A Smooth Transition Logit Model of the Effects of Deregulation in the Electricity Market," NCER Working Paper Series 100, National Centre for Econometric Research.
  3. Line Elvstrøm Ekner & Emil Nejstgaard, 2013. "Parameter Identification in the Logistic STAR Model," Discussion Papers 13-07, University of Copenhagen. Department of Economics.

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