摘要翻译：
逃税是指个人、公司和信托机构非法逃税。避税造成的收入损失会损害政府政策的有效性和公平性。逃税的一个标准衡量标准是税收差额，它可以估计为在一定时期内理论上可征收的税收总额与实际征收的税收总额之间的差额。本文提出了一种基于财政审计结果的自底向上方法的原创性贡献，通过使用机器学习。自下而上方法的主要缺点是，当被审计的纳税人不是随机选择时，就像意大利税务局进行的审计那样，存在选择偏差。我们的建议基于一个两步梯度增强模型，产生了一个稳健的税收差距估计，并嵌入了一个解决方案来纠正选择偏差，这不需要对潜在数据分布进行任何假设。根据从税务管理数据库收集的2011财政年度的财政和行政数据所得税申报单，采用两步梯度提升法估计意大利各公司的增值税缺口。与经典的参数预测方法相比，该方法在预测性能上有了明显的提高。
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英文标题：
《VAT tax gap prediction: a 2-steps Gradient Boosting approach》
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作者：
Giovanna Tagliaferri, Daria Scacciatelli, Pierfrancesco Alaimo Di Loro
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最新提交年份：
2020
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分类信息：

一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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一级分类：Economics        经济学
二级分类：General Economics        一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Quantitative Finance        数量金融学
二级分类：Economics        经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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英文摘要：
  Tax evasion is the illegal evasion of taxes by individuals, corporations, and trusts. The revenue loss from tax avoidance can undermine the effectiveness and equity of the government policies. A standard measure of tax evasion is the tax gap, that can be estimated as the difference between the total amounts of tax theoretically collectable and the total amounts of tax actually collected in a given period. This paper presents an original contribution to bottom-up approach, based on results from fiscal audits, through the use of Machine Learning. The major disadvantage of bottom-up approaches is represented by selection bias when audited taxpayers are not randomly selected, as in the case of audits performed by the Italian Revenue Agency. Our proposal, based on a 2-steps Gradient Boosting model, produces a robust tax gap estimate and, embeds a solution to correct for the selection bias which do not require any assumptions on the underlying data distribution. The 2-steps Gradient Boosting approach is used to estimate the Italian Value-added tax (VAT) gap on individual firms on the basis of fiscal and administrative data income tax returns gathered from Tax Administration Data Base, for the fiscal year 2011. The proposed method significantly boost the performance in predicting with respect to the classical parametric approaches.
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PDF链接：
https://arxiv.org/pdf/1912.03781