Estimation of dynamic models of recurring events with censored data

Tue Gorgens and Sanghyeok Lee

ANU Working Papers in Economics and Econometrics from Australian National University, College of Business and Economics, School of Economics

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.

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. (search for similar items in EconPapers)
JEL-codes: C33 C41 C51 (search for similar items in EconPapers)
Date: 2017-11
New Economics Papers: this item is included in nep-ecm and nep-ore
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:acb:cbeeco:2017-655

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