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Outliers and persistence in threshold autoregressive processes

Author

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  • Ahmad Yamin

    (Department of Economics, University of Wisconsin – Whitewater, 800 W Main Street, Whitewater, WI 53190, USA)

  • Donayre Luiggi

    () (Department of Economics, University of Minnesota – Duluth, 1318 Kirby Drive, Duluth, MN 55812, USA)

Abstract

This paper uses Monte Carlo simulations to investigate the effects of outlier observations on the properties of linearity tests against threshold autoregressive (TAR) processes. By considering different specifications and levels of persistence for the data-generating processes, we find that additive outliers distort the size of the test and that the distortion increases with the level of persistence. In addition, we also find that larger additive outliers can help to improve the power of the test in the case of persistent TAR processes.

Suggested Citation

  • Ahmad Yamin & Donayre Luiggi, 2016. "Outliers and persistence in threshold autoregressive processes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(1), pages 37-56, February.
  • Handle: RePEc:bpj:sndecm:v:20:y:2016:i:1:p:37-56:n:4
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    References listed on IDEAS

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    Cited by:

    1. repec:eee:ecmode:v:68:y:2018:i:c:p:273-292 is not listed on IDEAS
    2. Hirsch, Tristan & Rinke, Saskia, 2017. "Changes in Persistence in Outlier Contaminated Time Series," Hannover Economic Papers (HEP) dp-583, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.

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