论文标题
动态离散选择中的近似刺激性推断
Approximation-Robust Inference in Dynamic Discrete Choice
论文作者
论文摘要
动态离散选择模型的估计和推断通常依赖于近似来降低动态编程的计算负担。不幸的是，近似值的使用会赋予估计的实质性偏见，并导致无效的置信度集。我们提出了一种用于设置估计和推理的方法，该方法明确说明了使用近似值，因此无论近似误差如何，都是有效的。我们展示了如何以低计算成本来解释近似值的错误。我们的方法使研究人员可以评估由于使用近似值而导致的估计错误，从而更有效地管理偏见和计算方便之间的权衡。我们提供模拟证据以证明我们方法的实用性。
Estimation and inference in dynamic discrete choice models often relies on approximation to lower the computational burden of dynamic programming. Unfortunately, the use of approximation can impart substantial bias in estimation and results in invalid confidence sets. We present a method for set estimation and inference that explicitly accounts for the use of approximation and is thus valid regardless of the approximation error. We show how one can account for the error from approximation at low computational cost. Our methodology allows researchers to assess the estimation error due to the use of approximation and thus more effectively manage the trade-off between bias and computational expedience. We provide simulation evidence to demonstrate the practicality of our approach.
