A kernelization-based approach to nonparametric binary choice models
非参数二元选择模型的核化估计方法AI 解读 ↓
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非参数二元选择模型的核化估计方法
- 研究问题
- 如何在不对协变量系统函数和误差分布施加参数结构的情况下估计二元选择模型，并控制协变量较多时的计算规模？
- 方法与思路
- 作者将特定再生核希尔伯特空间视为筛空间，结合谱截断正则化降维，并研究加权平均偏导数的代入估计。
- 材料陈述的贡献
- 摘要称，估计量一致且偏导数估计渐近正态；模拟显示误设时表现改善、正确设定时效率损失较小，并提供庇护裁决应用。
- 阅读关注点
- 应核对二元选择模型的识别与规范化、核及谱截断选择、计算扩展性，以及天气与裁决关系在应用中的解释依据。
- 阅读边界
- 仅据所给 OpenAlex 摘要，无法确认识别条件、效率损失大小或温度效应的因果性质；不能推断后续刊本变化。
原文摘要
We propose a new estimator for nonparametric binary choice models that does not impose a parametric structure on either the systematic function of covariates or the distribution of the error term. A key advantage of our approach is its computational scalability in the number of covariates. For instance, even when assuming a normal error distribution as in probit models, commonly used sieves for approximating an unknown function of covariates can lead to a large-dimensional optimization problem when the number of covariates is moderate. Our approach, motivated by kernel methods in machine learning, views certain reproducing kernel Hilbert spaces as special sieve spaces, coupled with spectral cut-off regularization for dimension reduction. We establish the consistency of the proposed estimator and asymptotic normality of the plug-in estimator for weighted average partial derivatives. Simulation studies show that, compared to parametric estimation methods, the proposed method effectively improves finite sample performance in cases of misspecification, and has a rather mild efficiency loss if the model is correctly specified. Using administrative data on the grant decisions of US asylum applications to immigration courts, along with nine case-day variables on weather and pollution, we re-examine the effect of outdoor temperature on court judges' ``mood'', and thus, their grant decisions.