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平稳动态离散选择模型的非参数识别AI 解读

We provide new non-parametric identification results for stationary dynamic discrete choice models, where both the flow utilities and the distribution of unobserved shocks are fully non-parametric. Our main identification result establishes that a multinomial choice model is non-parametrically identified when there is a special regressor that (i) has a known derivative in the utility function (e.g., enters utility quasi-linearly); (ii) only affects the evolution of the other…

动态离散博弈中高效且收敛的序贯伪似然估计AI 解读

Abstract We propose a new sequential Efficient Pseudo-Likelihood (k-EPL) estimator for dynamic discrete choice games of incomplete information. k-EPL considers the joint behaviour of multiple players simultaneously, as opposed to individual responses to other agents’ equilibrium play. This, in addition to reframing the problem from conditional choice probability (CCP) space to value function space, yields a computationally tractable, stable, and efficient estimator. We show…