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A Monte Carlo Comparison of Time Varying Parameter and Multiprocess Mixture Models in the Presence of Structural Shifts and Outliers

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  • Gamble, James A
  • LeSage, James P

Abstract

This Monte Carlo study compares the performance of a recently proposed multiprocess mixture model and a more traditional random walk time-varying parameter model in the face of structural shifts and outliers. The mixture model performs well and the latter model performs poorly. This finding is of general interest since investigators often adopt random-walk time-varying parameter models to accommodate potential regime shifts in regression relationships. The findings suggest that the time-varying parameter estimation procedure is unlikely to find abrupt shifts, since the time-varying parameter estimates are contaminated by the outliers and regime shifts. Copyright 1993 by MIT Press.

Suggested Citation

  • 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 515-519, August.
    • Handle: RePEc:tpr:restat:v:75:y:1993:i:3:p:515-19
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    Cited by:

    1. Haroon Mumtaz & Konstantinos Theodoridis, 2014. "The Changing Transmission of Uncertainty shocks in the US: An Empirical Analysis," Working Papers 735, Queen Mary University of London, School of Economics and Finance.
    2. Haroon Mumtaz, 2016. "The Evolving Transmission of Uncertainty Shocks in the United Kingdom," Econometrics, MDPI, vol. 4(1), pages 1-18, March.
    3. Haroon Mumtaz & Konstantinos Theodoridis, 2014. "The Changing Transmission of Uncertainty shocks in the US: An Empirical Analysis," Working Papers 735, Queen Mary University of London, School of Economics and Finance.
    4. Terence Chong, 2001. "Estimating the locations and number of change points by the sample-splitting method," Statistical Papers, Springer, vol. 42(1), pages 53-79, January.

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