Regularization for Nonlinear Panel Models, Estimation of Heterogeneous Taxable Income Elasticities, and Conditional Influence Functions

个人的需求（偏好）和能力可能在一定程度上决定价格或税率。由此产生的价格（或税收）和优惠的同时变化使得很难估计税收或价格变化的政策影响。面板数据是对个体代理人的重复观察，可以帮助隔离价格（或税收）变化的政策影响。如果价格随时间而变化，而偏好是稳定的，那么选择随时间的变化可以归因于价格变化。拟议的研究将使用三个项目来开发面板数据方法，以估计价格和税率变化对经济成果和福利的影响。其中一个项目将使用大数据方法灵活建模偏好和价格之间的关系，同时施加很少的约束。所得结果将用于估计物价变动的福利效应。第二个项目将估计应税收入选择的面板数据模型，因为税收表允许比以前的工作更普遍的异质性。该方法将应用于税收政策评估。第三个项目将开发可用于检查其他项目结果敏感性的新方法。对于面板数据，在给定价格（或税率）的所有时间段内，个人偏好的分布是一个重要的、未知的干扰函数，当时间段数量T中等或较大时，它是高维的。然而，个体偏好的时不变性限制了近似讨厌函数所需系数的大小，这表明限制系数的大小在实践中可能是有用的。本研究将使用这些限制来估计中等到大t的面板数据模型。此外，应税所得（ETI）相对于净税率的弹性是预测税制改革效果和设计所得税的关键参数。最近的证据表明，个体之间的ETI存在巨大的异质性。拟议的研究将使用面板数据来估计和分析个别特定的ETI。拟议的研究还将开发和分析条件影响函数。这项工作将扩展影响函数分析来估计局部灵敏度，并构建可用于去偏机器学习的估计方程。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Individual wants (preferences) and abilities may partly determine prices or tax rates. The resulting simultaneous changes in prices (or taxes) and preferences make it difficult to estimate policy effects of tax or price changes. Panel data, which are repeated observations on individual agents, can help isolate policy effects of price (or tax) changes. If prices change over time while preferences are stable then variation in choices over time can be attributed to price changes. The proposed research will use three projects to develop panel data methods to estimate the effect of price and tax rate changes on economic outcomes and welfare. One project will use big data methods to flexibly model the relationship between preferences and prices while imposing few constraints. The results will be applied to estimate welfare effects of price changes. The second project will estimate a panel data model of taxable income choice given the tax schedule that allows more general heterogeneity than in previous work. This approach will be applied for tax policy evaluation. The third project will develop new methods that can be used to check sensitivity of results from other projects. With panel data, the distribution of individual preferences given prices (or tax rates) in all time periods is an important, unknown nuisance function that is high dimensional when the number of time periods T is moderate or large. However, time invariance of individual preferences restricts the size of coefficients needed to approximate the nuisance function, suggesting that restricting the size of the coefficients could be useful in practice. The proposed research will use such restrictions to estimate panel data models for moderate to large T. Also, the elasticity of taxable income (ETI) with respect to the net of tax rate is a key parameter for predicting the effect of tax reform and designing income taxes. Recent evidence points to substantial heterogeneity in the ETI across individuals. The proposed research will use panel data to estimate and analyze individual specific ETI’s. The proposed research will also develop and analyze conditional influence functions. This work will extend influence function analysis to estimate local sensitivity and construct estimating equations that can be used in debiased machine learning.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.