no code implementations • 13 Feb 2024 • Jackson Bunting, Paul Diegert, Arnaud Maurel
We provide semiparametric identification results for a broad class of learning models in which continuous outcomes depend on three types of unobservables: i) known heterogeneity, ii) initially unknown heterogeneity that may be revealed over time, and iii) transitory uncertainty.
no code implementations • 5 Apr 2023 • Jackson Bunting, Takuya Ura
Our proposed estimator uses the equality constraints to decrease the dimension of the optimization problem, thereby generating computational gains.
no code implementations • 8 Feb 2022 • Jackson Bunting
In dynamic discrete choice (DDC) analysis, it is common to use mixture models to control for unobserved heterogeneity.
no code implementations • 5 Oct 2020 • Federico A. Bugni, Jackson Bunting, Takuya Ura
We refer to this as the "homogeneity assumption" in dynamic discrete games.