Estimation of dynamic models of recurrent events with censored data

Sanghyeok Lee and Tue Gørgens

The Econometrics Journal, 2021, vol. 24, issue 2, 199-224

Abstract: SummaryIn this paper, we consider estimation of dynamic models of recurrent 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 this 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. In an empirical application, we study ischaemic heart disease events for male Maoris in New Zealand.

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; ischaemic heart disease; Maori (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:oup:emjrnl:v:24:y:2021:i:2:p:199-224.

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