Arbeitspapier
Dynamic mixture-of-experts models for longitudinal and discrete-time survival data
We propose a general class of flexible models for longitudinal data with special emphasis on discrete-time survival data. The model is a finite mixture model where the subjects are allowed to move between components through time. The time-varying probability of component memberships is modeled as a function of subject-specific time-varying covariates. This allows for interesting within-subject dynamics and manageable computations even with a large number of subjects. Each parameter in the component densities and in the mixing function is connected to its own set of covariates through a link function. The models are estimated using a Bayesian approach via a highly efficient Markov Chain Monte Carlo (MCMC) algorithm with tailored proposals and variable selection in all set of covariates. The focus of the paper is on models for discrete-time survival data with an application to bankruptcy prediction for Swedish firms, using both exponential and Weibull mixture components. The dynamic mixture-of-experts models are shown to have an interesting interpretation and to dramatically improve the out-of-sample predictive density forecasts compared to models with time-invariant mixture probabilities.
- Sprache
-
Englisch
- Erschienen in
-
Series: Sveriges Riksbank Working Paper Series ; No. 268
- Klassifikation
-
Wirtschaft
Bayesian Analysis: General
Duration Analysis; Optimal Timing Strategies
Firm Behavior: Theory
Bankruptcy; Liquidation
- Thema
-
Bayesian inference
Markov Chain Monte Carlo
Bayesian variable selection
Survival Analysis
Mixture-of-experts
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Quiroz, Matias
Villani, Mattias
- Ereignis
-
Veröffentlichung
- (wer)
-
Sveriges Riksbank
- (wo)
-
Stockholm
- (wann)
-
2013
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Quiroz, Matias
- Villani, Mattias
- Sveriges Riksbank
Entstanden
- 2013
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