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Testing for State Dependence with Time-Variant Transition Probabilities

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  • Timothy Halliday

Abstract

We derive a simple result that allows us to test for the presence of state dependence in a dynamic Logit model with time-variant transition probabilities and an arbitrary distribution of the unobserved heterogeneity. Monte Carlo evidence suggests that this test has desirable properties even when there are some violations of the model's assumptions. We also consider alternative tests that will have desirable properties only when the transition probabilities do not depend on time and provide evidence that there is an “acceptable” range in which ignoring time-dependence does not matter too much. We conclude with an application to the Barker Hypothesis.

Suggested Citation

  • Timothy Halliday, 2007. "Testing for State Dependence with Time-Variant Transition Probabilities," Econometric Reviews, Taylor & Francis Journals, vol. 26(6), pages 685-703.
    • Handle: RePEc:taf:emetrv:v:26:y:2007:i:6:p:685-703
    DOI: 10.1080/07474930701653768
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    Cited by:

    1. Francesco Bartolucci & Valentina Nigro & Claudia Pigini, 2018. "Testing for state dependence in binary panel data with individual covariates by a modified quadratic exponential model," Econometric Reviews, Taylor & Francis Journals, vol. 37(1), pages 61-88, January.
    2. Bartolucci, Francesco & Nigro, Valentina & Pigini, Claudia, 2013. "Testing for state dependence in binary panel data with individual covariates," MPRA Paper 48233, University Library of Munich, Germany.
    3. Halliday Timothy, 2011. "Health Inequality over the Life-Cycle," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 11(3), pages 1-21, October.
    4. Timothy J. Halliday, 2008. "Heterogeneity, state dependence and health," Econometrics Journal, Royal Economic Society, vol. 11(3), pages 499-516, November.

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