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.
Discrete choice; heterogeneity; Markov process; mixture; state dependence;
- C14: Semiparametric and Nonparametric Methods: General
- C23: Panel Data Models • Spatio-temporal Models
- C51: Model Construction and Estimation
Ayden Higgins, and Koen Jochmans, “Identification Of Mixtures Of Dynamic Discrete Choices”, TSE Working Paper, n. 21-1272, November 23, 2021.
TSE Working Paper, n. 21-1272, November 23, 2021