AI 中文总结
研究随机系数分布平均泛函推断程序，利用惩罚固定网格估计器非参数估计分布，建立渐近正态性及考虑正则化偏差的置信区间，适用于多种泛函，模拟和实证显示方法有效且能产生有意义差异。
AI 中文摘要
本文针对随机系数分布的平均泛函（如平均支付意愿和平均弹性）开发了一种推断程序，当使用Heiss、Hetzenecker和Osterhaus（2022）的惩罚固定网格估计器非参数估计分布时。我们建立了以在惩罚伪真值处评估的泛函为中心的相应惩罚插件估计器的渐近正态性，并提出了一个考虑正则化偏差的置信区间。我们的方法适用于广泛的线性和非线性泛函，允许研究人员使用密集网格来减少近似偏差，同时保持有效推断。蒙特卡罗模拟表明，所提出的区间在有限样本中实现了接近名义水平的覆盖率，同时保持信息性。对出行方式需求的实证应用表明，灵活的非参数规范相对于标准参数模型可以产生经济上有意义的差异。
英文摘要
This paper develops an inference procedure for average functionals of random-coefficient distributions, such as mean willingness-to-pay and average elasticities, when the distribution is estimated nonparametrically using the penalized fixed-grid estimator of Heiss, Hetzenecker, and Osterhaus (2022). We establish asymptotic normality of the corresponding penalized plug-in estimator centered at the functional evaluated at the penalized pseudo-true value and propose a confidence interval that accounts for the regularization bias. Our method applies to a broad class of linear and nonlinear functionals and allows researchers to use dense grids to reduce approximation bias while maintaining valid inference. Monte Carlo simulations show that the proposed intervals achieve coverage close to the nominal level while remaining informative in finite samples. An empirical application to travel mode demand illustrates that flexible nonparametric specifications can yield economically meaningful differences relative to standard parametric models.
Comments45 pages (24 pages excluding references and appendices), 9 figures (4 figures in main text), 5 tables (2 tables in main text), presented at NESG in Tilburg in May 2026 and IAAE in Lisbon in June 2026
