Asymptotic Theory for Regressions with Smoothly Changing Parameters

Eric Hillebrand 1 , Marcelo C. Medeiros 2  and Junyue Xu 3
  • 1 CREATES, Aarhus University, Aarhus, Denmark
  • 2 Department of Economics, Pontifical Catholic University of Rio De Janeiro, Rio de Janeiro, RJ, Brazil
  • 3 MFE Program, Haas School of Business, University of California Berkeley, Berkeley, CA, USA
Eric Hillebrand, Marcelo C. Medeiros and Junyue Xu

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 -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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The Journal of Time Series Econometrics (JTSE) serves as an internationally recognized outlet for important new research in both theoretical and applied classical and Bayesian time series, spatial and panel data econometrics. The scope of the journal includes papers dealing with estimation, testing and other methodological aspects involved in the application of time series and spatial analytic techniques to economic, financial and related data.

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