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.

Language
Englisch

Bibliographic citation
Series: Sveriges Riksbank Working Paper Series ; No. 268

Classification
Wirtschaft
Bayesian Analysis: General
Duration Analysis; Optimal Timing Strategies
Firm Behavior: Theory
Bankruptcy; Liquidation
Subject
Bayesian inference
Markov Chain Monte Carlo
Bayesian variable selection
Survival Analysis
Mixture-of-experts

Event
Geistige Schöpfung
(who)
Quiroz, Matias
Villani, Mattias
Event
Veröffentlichung
(who)
Sveriges Riksbank
(where)
Stockholm
(when)
2013

Handle
Last update
10.03.2025, 11:43 AM CET

Data provider

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Object type

  • Arbeitspapier

Associated

  • Quiroz, Matias
  • Villani, Mattias
  • Sveriges Riksbank

Time of origin

  • 2013

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