标题： Bootstrap推断在部分线性二元选择模型中 显示英文标题

标题： Bootstrap Inference on Partially Linear Binary Choice Model

摘要： 部分线性二元选择模型可用于估计结构方程，其中非线性可能由于边际收益递减、不同的生命周期阶段或繁忙的物理现象而出现。 该模型的推断过程基于分析渐近近似，在样本量不是足够大时，有限样本中的推断可能不可靠。 本文提出了该模型的引导推断方法。 蒙特卡洛模拟显示，与基于渐近近似的过程相比，所提出的推断方法在有限样本中表现良好。 显示英文摘要

摘要： The partially linear binary choice model can be used for estimating structural equations where nonlinearity may appear due to diminishing marginal returns, different life cycle regimes, or hectic physical phenomena. The inference procedure for this model based on the analytic asymptotic approximation could be unreliable in finite samples if the sample size is not sufficiently large. This paper proposes a bootstrap inference approach for the model. Monte Carlo simulations show that the proposed inference method performs well in finite samples compared to the procedure based on the asymptotic approximation.