Abstract
In dynamic discrete choice analysis in econometrics, controlling for unobserved heterogeneity is an important issue. Finite mixture models provide flexible ways to account for it, and have been used in numerous applications. However, until recently, pessimistic views were prevalent on the nonparametric identifiability of finite mixture models of dynamic discrete choices, because little was known of nonparametric identifiability of mixture models. We review Kasahara and Shimotsu (2009, 2014a), and introduce sufficient conditions for nonparametric identification for various finite mixture models of dynamic discrete choices used in applied work under different assumptions on the time-dimension of panel data, stationarity, and Markov property. We also discuss nonparametric identifiability of the lower bound for the number of components in finite mixture models and provide an estimation procedure for the number of components.
