Estimation of dynamic models of recurrent events with censored data
| Autor: | Tue Gørgens, Sanghyeok Lee |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Economics and Econometrics
Computer science Listwise deletion 05 social sciences Monte Carlo method Estimator Random effects model Missing data symbols.namesake 0502 economics and business symbols Gaussian quadrature 050207 economics Algorithm Importance sampling 050205 econometrics Event (probability theory) |
| Zdroj: | The Econometrics Journal. 24:199-224 |
| ISSN: | 1368-423X 1368-4221 |
| Popis: | Summary In 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. |
| Databáze: | OpenAIRE |
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