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)和偏好的同时变化，使得很难估计税收或价格变化的政策影响。面板数据是对单个因素的反复观察，可以帮助隔离价格(或税收)变化的政策影响。如果价格随着时间的推移而变化，而偏好是稳定的，那么选择随时间的变化可以归因于价格的变化。这项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对偏好和价格之间的关系进行建模，同时施加的约束很少。结果将被应用于估计价格变化的福利影响。第二个项目将估计一个应税收入选择的面板数据模型，给定的纳税时间表允许比以前的工作更普遍的异质性。该方法将被应用于税收政策评估。第三个项目将开发新的方法，可用于检查其他项目结果的敏感性。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、未知的滋扰函数，当时间段T的数量为中等或较大时，该函数是高维的。然而，个体偏好的时间不变性限制了近似干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。建议的研究将使用这些限制来估计中到大T的面板数据模型，并且应纳税所得额(ETI)相对于税率净额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，ETI在不同个体之间存在显著的异质性。建议的研究将使用面板数据来估计和分析个别特定的ETI。建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度，并构建可用于去偏向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智力优势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.