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Comparison of Two Alternative Approaches to Modeling Level Shifts in the Presence of Outliers

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  • Prasad Bidarkota

    ()
    (Department of Economics, Florida International University)

Abstract

We study alternative models for capturing abrupt structural changes (level shifts) in a times series. The problem is confounded by the presence of transient outliers. We compare the performance of non-Gaussian time-varying parameter models and multiprocess mixture models within a Monte Carlo experimental setup. Our findings suggest that once we incorporate shocks with thick-tailed probability distributions, the superiority of the multiprocess mixture models over the time-varying parameter models, reported in an earlier study, disappears. The behavior of the two models, both in adapting to level shifts and in reacting to transient outliers, is very similar.

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File URL: http://casgroup.fiu.edu/pages/docs/2248/1280267800_03-07.pdf
File Function: First version, 2003
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Bibliographic Info

Paper provided by Florida International University, Department of Economics in its series Working Papers with number 0307.

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Length: 18 pages
Date of creation: Jul 2003
Date of revision:
Publication status: Published in Communications in Statistics: Simulation and Computation, 33(3):661-671, (2004).
Handle: RePEc:fiu:wpaper:0307

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Related research

Keywords: time-varying parameter (TVP) models; non-Gaussian state space models; multiprocess mixture models; level shifts; outliers;

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  1. Prasad V. Bidarkota & J. Huston McCulloch, 1998. "Optimal univariate inflation forecasting with symmetric stable shocks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(6), pages 659-670.
  2. Tanizaki, Hisashi & Mariano, Roberto S., 1998. "Nonlinear and non-Gaussian state-space modeling with Monte Carlo simulations," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 263-290.
  3. J. Durbin & S. J. Koopman, 2000. "Time series analysis of non-Gaussian observations based on state space models from both classical and Bayesian perspectives," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(1), pages 3-56.
  4. Gamble, James A & LeSage, James P, 1993. "A Monte Carlo Comparison of Time Varying Parameter and Multiprocess Mixture Models in the Presence of Structural Shifts and Outliers," The Review of Economics and Statistics, MIT Press, vol. 75(3), pages 515-19, August.
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