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

个人需求（偏好）和能力可能部分决定价格或税率。由此产生的价格（或税收）和优惠的同时变化使得难以估计税收或价格变化的政策影响。面板数据是对单个行为体的重复观察，可以帮助隔离价格（或税收）变化的政策影响。如果价格随时间变化，而偏好是稳定的，那么随着时间的推移，选择的变化可以归因于价格的变化。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结果和福利的影响。其中一个项目将使用大数据方法灵活地模拟偏好和价格之间的关系，同时施加很少的限制。研究结果将被应用于估计价格变化的福利效应。第二个项目将估计一个面板数据模型的应税收入选择的税收时间表，允许更多的一般异质性比以前的工作。这种方法将被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，给定价格（或税率）的个人偏好在所有时间段的分布是一个重要的未知滋扰函数，当时间段T的数量是中等或大时，该函数是高维的。然而，个人偏好的时间不变性限制了近似滋扰函数所需的系数的大小，这表明限制系数的大小在实践中可能是有用的。拟议的研究将使用这种限制，估计面板数据模型的中到大T。应税收入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重要参数。最近的证据表明，ETI在个体之间存在很大的异质性。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，以估计局部敏感性，并构建可用于去偏机器学习的估计方程。该奖项反映了NSF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.