Résumé
This paper provides new identification results for finite mixtures of Markov processes. Our arguments yield identification from knowledge of the cross-sectional distribution of three (or more) effective time-series observations under simple conditions. We explain how our approach and results are different from those in previous work by Kasahara and Shimotsu (2009) and Hu and Shum (2012). Most notably, outside information, such as monotonicity restrictions that link conditional distributions to latent types, is not needed.
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
Remplace
Ayden Higgins et Koen Jochmans, « Identification Of Mixtures Of Dynamic Discrete Choices », TSE Working Paper, n° 21-1272, 23 novembre 2021, révision janvier 2023.
Référence
Ayden Higgins et Koen Jochmans, « Identification of Mixtures of Dynamic Discrete Choices », Journal of Econometrics, vol. 237, n° 1, 105462, novembre 2023.
Voir aussi
Publié dans
Journal of Econometrics, vol. 237, n° 1, 105462, novembre 2023