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

非线性面板模型的正则化、异质应税收入弹性的估计和条件影响函数

• 批准号: 2242447
• 负责人: Whitney Newey
• 金额: $ 19.57万
• 依托单位: National Bureau of Economic Research Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2242447&HistoricalAwards=false
• 关键词: Regularization; Nonlinear; Panel; Models; Estimation

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.

个人的需求（偏好）和能力可能会部分影响价格（或税收）和偏好的同时变化，因此很难估计税收或价格变化的政策影响，而面板数据是对确定的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，如果价格随时间变化而偏好稳定，则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来估计。价格和税率变化对经济成果和福利的影响 一。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，同时施加很少的约束，其结果将用于估计价格变化的福利影响。第二个项目将估计给定税收表的应税收入选择的面板数据模型。第三个项目将开发新的方法，可用于检查其他项目的结果的敏感性，即给定的个人偏好的分布。所有时间段内的价格（或税率）是当时间段 T 的数量适中或较大时，该函数是一个重要的、未知的高维函数。然而，个体偏好的时间不变性限制了逼近该函数所需的系数的大小，这表明可以限制系数的大小。拟议的研究将使用此类限制来估计中型至大型 T 的面板数据模型。此外，应税收入 (ETI) 相对于净税率的弹性是预测税收影响的关键参数。改革最近的证据表明，不同个体之间的 ETI 存在显着异质性。拟议的研究还将开发和分析条件影响函数。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。