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Heterogeneity, Uncertainty and Learning: Semiparametric Identification and Estimation
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.
Faster estimation of dynamic discrete choice models using index sufficiency
Our proposed estimator uses the equality constraints to decrease the dimension of the optimization problem, thereby generating computational gains.
Continuous permanent unobserved heterogeneity in dynamic discrete choice models
In dynamic discrete choice (DDC) analysis, it is common to use mixture models to control for unobserved heterogeneity.
Testing homogeneity in dynamic discrete games in finite samples
We refer to this as the "homogeneity assumption" in dynamic discrete games.
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