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Diagnostic analysis and computational strategies for estimating discrete time duration models—A Monte Carlo study
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Diagnostic analysis and computational strategies for estimating discrete time duration models—A Monte Carlo study

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  • Li, Xianghong
  • Smith, Barry

Abstract

This paper uses Monte Carlo analysis to study important and contentious issues in estimating single-spell discrete time duration models. We find simulated annealing dominates gradient methods for recovering true models. We recommend a partially flexible step function for duration dependence combined with likelihood ratio tests for determining support points of unobserved heterogeneity. We find that ignoring time-changing features of explanatory variables introduces substantial biases in model coefficient and average partial effect estimates. These biases do not diminish as sample size increases.

Suggested Citation

  • Li, Xianghong & Smith, Barry, 2015. "Diagnostic analysis and computational strategies for estimating discrete time duration models—A Monte Carlo study," Journal of Econometrics, Elsevier, vol. 187(1), pages 275-292.
    • Handle: RePEc:eee:econom:v:187:y:2015:i:1:p:275-292
    DOI: 10.1016/j.jeconom.2015.02.024
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    More about this item

    Keywords

    Discrete time duration model; Monte Carlo; Simulated annealing; Duration dependence; Unobserved heterogeneity;
    All these keywords.

    JEL classification:

    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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