Abstract
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.
Keywords
discrete choice; heterogeneity; Markov process; mixture; state dependence;
JEL codes
- C14: Semiparametric and Nonparametric Methods: General
- C23: Panel Data Models • Spatio-temporal Models
- C51: Model Construction and Estimation
Replaces
Ayden Higgins, and Koen Jochmans, “Identification Of Mixtures Of Dynamic Discrete Choices”, TSE Working Paper, n. 21-1272, November 23, 2021, revised January 2023.
Reference
Ayden Higgins, and Koen Jochmans, “Identification of Mixtures of Dynamic Discrete Choices”, Journal of Econometrics, vol. 237, n. 1, 105462, November 2023.
See also
Published in
Journal of Econometrics, vol. 237, n. 1, 105462, November 2023