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主讲人 |
Shuo Jiang |
简介 |
<p>Sequential estimation and inference in static games of incomplete information with non-separable unobserved heterogeneity face two main challenges: the matching-types problem and large state space. We tackle the matching-types problem by constructing a new characterization of the payoff function via a minimum distance model with incorrect moments. Large state space leads to large number of moments, rendering optimization via exhaustive search computationally challenging. We propose a new multi-step estimation procedure and demonstrate via extensive simulation that it is extremely fast to compute. Theoretically our estimator selects the correct matching with probability approaching one and consistently estimates the payoff function. We propose an asymptotically uniformly valid and easy-to-implement test for the linear hypothesis on the payoff function.</p> |
