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Identifying State Dependence in Non-Stationary Processes

Author

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  • Timothy Halliday

    (Department of Economics, University of Hawaii at Manoa
    John A. Burns School of Medicine, University of Hawaii at Manoa)

Abstract

We consider the identification of state dependence in a non-stationary process of binary outcomes within the context of the dynamic logit model with time-variant transition probabilities and an arbitrary distribution for the unobserved heterogeneity. We derive a simple identification result that allows us to calculate a test for state dependence in this model. We also consider alternative tests for state dependence that will have desirable properties only in stationary processes and derive their asymptotic properties when the true underlying process is non-stationary. Finally, we provide Monte Carlo evidence that shows a range of non-stationarity in which the effects of mis-specifying the binary process as stationary are not too large.

Suggested Citation

  • Timothy Halliday, 2006. "Identifying State Dependence in Non-Stationary Processes," Working Papers 200601, University of Hawaii at Manoa, Department of Economics.
    • Handle: RePEc:hai:wpaper:200601
    as

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    File URL: http://www.economics.hawaii.edu/research/workingpapers/WP_06-1.pdf
    File Function: First version, 2006
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    References listed on IDEAS

    as
    1. Bo E. Honoré & Elie Tamer, 2002. "Bounds on Parameters in Dynamic Discrete Choice Models," CAM Working Papers 2004-23, University of Copenhagen. Department of Economics. Centre for Applied Microeconometrics, revised Aug 2004.
    2. Hahn, Jinyong, 2001. "The Information Bound Of A Dynamic Panel Logit Model With Fixed Effects," Econometric Theory, Cambridge University Press, vol. 17(5), pages 913-932, October.
    3. Thierry Magnac, 2004. "Panel Binary Variables and Sufficiency: Generalizing Conditional Logit," Econometrica, Econometric Society, vol. 72(6), pages 1859-1876, November.
    4. Bo E. Honoré & Ekaterini Kyriazidou, 2000. "Panel Data Discrete Choice Models with Lagged Dependent Variables," Econometrica, Econometric Society, vol. 68(4), pages 839-874, July.
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    More about this item

    Keywords

    Dynamic Panel Data Models; State Dependence; Non-Stationary Processes;
    All these keywords.

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