Arbeitspapier
A Monte Carlo study on non-parametric estimation of duration models with unobserved heterogeneity
We conduct extensive Monte Carlo experiments on non-parametric estimations of duration models with unknown duration dependence and unknown mixing distribution for unobserved heterogeneity. We propose a full non-parametric maximum likelihood approach, based on time-varying lagged explanatory covariates from observational data. By utilising this data-based identification source, we find that both duration dependence and unobserved heterogeneity can be reliably estimated. Our Monte Carlo evidences show that variation in time-varying lagged explanatory variables contributes to the identification of both duration dependence and unobserved heterogeneity, especially when sample sizes are limited. For limited sample sizes, maximum penalised likelihood with information criteria seems to produce more accurate estimators than pure maximum likelihood. Our approach can be easily extended to multivariate competing risks model with dependent unobserved heterogeneities.
- Sprache
-
Englisch
- Erschienen in
-
Series: Memorandum ; No. 2003,25
- Klassifikation
-
Wirtschaft
Semiparametric and Nonparametric Methods: General
Statistical Simulation Methods: General
Duration Analysis; Optimal Timing Strategies
- Thema
-
duration dependence
unobserved heterogeneity
non-parametric estimation
Monte Carlo study
time-varying covariates
Monte-Carlo-Methode
Schätztheorie
Theorie
Heteroskedastizität
Statistische Bestandsanalyse
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Zhang, Tao
- Ereignis
-
Veröffentlichung
- (wer)
-
University of Oslo, Department of Economics
- (wo)
-
Oslo
- (wann)
-
2003
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:45 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Zhang, Tao
- University of Oslo, Department of Economics
Entstanden
- 2003
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