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Estimation of dynamic models of recurring events with censored data

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  • Tue Gorgens
  • Sanghyeok Lee

Abstract

In this paper we consider estimation of dynamic models of recurring events (event histories) in continuous time using censored data. We develop maximum simulated likelihood estimators where missing data are integrated out using Monte Carlo and importance sampling methods. We allow for random effects and integrate out the unobserved heterogeneity using a quadrature rule. In Monte Carlo experiments, we find that maximum simulated likelihood estimation is practically feasible and performs better than both listwise deletion and auxiliary modelling of initial conditions.

Suggested Citation

  • Tue Gorgens & Sanghyeok Lee, 2017. "Estimation of dynamic models of recurring events with censored data," ANU Working Papers in Economics and Econometrics 2017-655, Australian National University, College of Business and Economics, School of Economics.
    • Handle: RePEc:acb:cbeeco:2017-655
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    File URL: https://www.cbe.anu.edu.au/researchpapers/econ/wp655.pdf
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    More about this item

    Keywords

    Duration analysis; survival analysis; failure-time analysis; reliability analysis; event history analysis; hazard rates; data censoring; panel data; initial conditions; random effects; maximum simulated likelihood; Monte Carlo integration; importance sampling.;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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