Temporal-Difference Estimation of Dynamic Discrete Choice Models
研究了用时间差分学习估计动态离散选择模型的结构参数，提出两种快速算法，适用于连续或高维状态空间，无需指定转移密度，在动态博弈中计算优势更明显。
Abstract We study the use of Temporal-Difference learning for estimating the structural parameters in dynamic discrete choice models. Our algorithms are based on the conditional choice probability approach but use functional approximations to estimate various terms in the pseudo-log-likelihood function. We suggest two approaches: The first—linear semi-gradient—provides approximations to the recursive terms using basis functions. The second—Approximate Value Iteration—builds a sequence of approximations to the recursive terms by solving non-parametric estimation problems. Our approaches are fast and naturally allow for continuous and/or high-dimensional state spaces. Furthermore, they do not require specification of transition densities. In dynamic games, they avoid integrating over other players’ actions, further heightening the computational advantage. Our proposals can be paired with popular existing methods such as pseudo-maximum-likelihood, and we propose locally robust corrections for the latter to achieve parametric rates of convergence. Monte Carlo simulations confirm the properties of our algorithms in practice.