Approximate Solutions to Stochastic Dynamic Programs
研究了结构估计中求解随机动态规划的各种近似方法，发现用最大期望值近似期望最大值效果差，而基于分布假设精确求解即使假设有误误差也小。
This paper examines the properties of various approximation methods for solving stochastic dynamic programs in structural estimation problems. The problem addressed is evaluating the expected value of the maximum of available choices. The paper shows that approximating this by the maximum of expected values frequently has poor properties. It also shows that choosing a convenient distributional assumptions for the errors and then solving exactly conditional on the distributional assumption leads to small approximation errors even if the distribution is misspecified.