摘 要
随着大数据技术的快速发展，税收政策效果评估逐渐从传统定性分析转向精准量化研究。本研究旨在通过整合多源大数据资源，构建基于机器学习和统计建模的综合分析框架，以实现对税收政策实施效果的全面量化评估。研究选取了某地区增值税改革试点数据作为样本，运用随机森林、回归分析及因果推断等方法，深入剖析政策实施对不同行业、企业规模以及区域经济发展的差异化影响。结果表明，大数据驱动的分析方法能够显著提升政策效果评估的精确性和时效性，同时揭示出传统方法难以发现的微观层面作用机制。研究发现，增值税改革在促进中小企业发展方面成效显著，但对部分劳动密集型行业的短期冲击较大，需配套相应支持措施。本研究的主要创新点在于首次将大规模税务申报数据与宏观经济指标相结合，并引入时间序列预测模型校正外部干扰因素，从而提高了评估结果的可靠性。此外，研究提出的框架具有较强的普适性，可为其他财税政策的效果评估提供参考，为决策者制定更加科学合理的税收政策提供了重要依据。
关键词：税收政策效果评估；大数据分析；机器学习；增值税改革；因果推断
Quantitative Analysis of Tax Policy Effects Based on Big Data
Abstract: With the rapid development of big data technologies, the evaluation of tax policy effectiveness has gradually shifted from traditional qualitative analysis to precise quantitative research. This study aims to construct an integrated analytical fr amework based on machine learning and statistical modeling by consolidating multi-source big data resources, thereby enabling a comprehensive quantitative assessment of the implementation effects of tax policies. Using pilot data from a value-added tax (VAT) reform in a specific region as a sample, this study employs methods such as random forests, regression analysis, and causal inference to thoroughly examine the differentiated impacts of policy implementation across various industries, enterprise scales, and regional economic development. The results indicate that big data-driven analytical approaches can significantly enhance the accuracy and timeliness of policy effectiveness evaluations while uncovering micro-level mechanisms that are difficult to identify using traditional methods. It was found that VAT reform demonstrates significant achievements in promoting the development of small and medium-sized enterprises but imposes considerable short-term impacts on certain labor-intensive industries, necessitating complementary supportive measures. A major innovation of this study lies in its pioneering integration of large-scale tax declaration data with macroeconomic indicators, coupled with the introduction of time-series prediction models to correct for external interference factors, which enhances the reliability of the evaluation results. Moreover, the proposed fr amework exhibits strong versatility, providing a reference for assessing the effectiveness of other fiscal and taxation policies and offering critical support for policymakers in formulating more scientifically sound and reasonable tax policies.
Keywords: Tax Policy Effect Evaluation; Big Data Analysis; Machine Learning; Value-Added Tax Reform; Causal Inference
目 录
一、绪论 1
（一）研究背景与意义 1
（二）国内外研究现状综述 1
（三）研究方法与技术路线 1
二、大数据在税收政策分析中的应用框架 2
（一）大数据技术在税收领域的适用性 2
（二）税收政策效果量化分析的理论基础 2
（三）数据驱动的税收政策评估框架构建 3
三、基于大数据的税收政策效果实证分析 3
（一）数据来源与预处理方法 4
（二）量化模型的设计与验证 4
（三）实证结果及其经济解释 5
四、政策优化与大数据支持体系构建 5
（一）税收政策调整的方向与依据 5
（二）大数据支持下的政策模拟与预测 6
（三）提升政策效果的策略建议 6
结论 7
参考文献 8
致 谢 9
