Document de travail

Identification Of Mixtures Of Dynamic Discrete Choices

Ayden Higgins et Koen Jochmans

Résumé

This paper provides new identification results for finite mixtures of Markov processes. Our arguments are constructive and show that identification can be achieved from knowledge of the cross-sectional distribution of three (or more) effective time-series observations under simple conditions. Our approach is contrasted with the ones taken in prior work by Kasahara and Shimotsu (2009) and Hu and Shum (2012). Most notably, monotonicity restrictions that link conditional distributions to latent types are not needed. Maximum likelihood is considered for the purpose of estimation and inference. Implementation via the EM algorithm is straightforward. Its performance is evaluated in a simulation exercise.

Mots-clés

Discrete choice; heterogeneity; Markov process; mixture; state dependence;

Codes JEL

  • C14: Semiparametric and Nonparametric Methods: General
  • C23: Panel Data Models • Spatio-temporal Models
  • C51: Model Construction and Estimation

Référence

Ayden Higgins et Koen Jochmans, « Identification Of Mixtures Of Dynamic Discrete Choices », TSE Working Paper, n° 21-1272, 23 novembre 2021.

Voir aussi

Publié dans

TSE Working Paper, n° 21-1272, 23 novembre 2021