Conditional Choice Probability Estimation of Dynamic Discrete Choice Models With Unobserved Heterogeneity
将EM算法引入条件选择概率估计，处理动态离散选择问题中的未观测异质性，扩展了低成本替代全解方法的模型类别，蒙特卡洛结果验证了算法在计算时间和参数精度上的良好表现。
We adapt the expectation–maximization algorithm to incorporate unobserved heterogeneity into conditional choice probability (CCP) estimators of dynamic discrete choice problems. The unobserved heterogeneity can be time-invariant or follow a Markov chain. By developing a class of problems where the difference in future value terms depends on a few conditional choice probabilities, we extend the class of dynamic optimization problems where CCP estimators provide a computationally cheap alternative to full solution methods. Monte Carlo results confirm that our algorithms perform quite well, both in terms of computational time and in the precision of the parameter estimates.