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Estimating a semi-parametric duration model without specifying heterogeneity

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Listed author(s):
  • Hausman, Jerry A.
  • Woutersen, Tiemen
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    Abstract

    This paper presents a new estimator for the mixed proportional hazard model that allows for a nonparametric baseline hazard and time-varying regressors. In particular, this paper allows for discrete measurement of the durations as happens often in practice. The integrated baseline hazard and all parameters are estimated at the regular rate, N, where N is the number of individuals. A hazard model is a natural framework for time-varying regressors. In particular, if a flow or a transition probability depends on a regressor that changes with time, a hazard model avoids the curse of dimensionality that would arise from interacting the regressors at each point in time with one another. This paper also presents a new test to detect unobserved heterogeneity.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0304407613001619
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    Article provided by Elsevier in its journal Journal of Econometrics.

    Volume (Year): 178 (2014)
    Issue (Month): P1 ()
    Pages: 114-131

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    Handle: RePEc:eee:econom:v:178:y:2014:i:p1:p:114-131
    DOI: 10.1016/j.jeconom.2013.08.011
    Contact details of provider: Web page: http://www.elsevier.com/locate/jeconom

    Keywords: Mixed proportional hazard model; Time-varying regressors; Heterogeneity;

    Find related papers by JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies

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    Cited by:

    1. Burda, Martin & Harding, Matthew, 2014. "Environmental Justice: Evidence from Superfund cleanup durations," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PA), pages 380-401.
    2. Wolter, James Lewis, 2016. "Kernel estimation of hazard functions when observations have dependent and common covariates," Journal of Econometrics, Elsevier, vol. 193(1), pages 1-16.

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