发表机构
Florida International University（佛罗里达国际大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于卷积的分层属性估计器，用于研发税收抵免研究中合格研究费用的确定，通过分层抽样解决向上偏差，并生成有效的95%置信下界。
AI 中文摘要
《一项宏大、美丽的法案》恢复了国内研究费用的立即费用化，减轻了税收负担，并激励了对美国本土研发（包括广泛行业中的软件开发）的再投资。在此背景下，准确且可辩护的合格研究费用（QRE）估计变得尤为重要。统计抽样为估计根据《国内税收法典》第41条（表格6765）记录的明确定义的业务组成部分（抽样框）总体的QRE提供了一种实用框架。美国国税局收入程序2011-42允许使用属性统计方法和变量统计方法，尽管后者（分层均值和差值估计器）通常被视为QRE估计的标准方法。在本文中，我们在模拟研究中比较了属性统计方法和变量统计方法，并证明属性方法具有若干理论和实践优势。简单属性估计器的一个关键问题是，由于抽样框中高潜在QRE（pQRE）非合格项目占主导地位，可能导致QRE确定出现向上偏差。我们通过引入一种分层抽样设计来解决这一问题，该设计确保高pQRE项目在样本中得到充分代表。我们证明，基于卷积的分层属性估计器在一系列抽样框结构下，能够对总QRE产生有效的一侧95%置信下界。
英文摘要
The One Big, Beautiful Bill Act reinstates immediate expensing of domestic research expenses, reducing the tax burden and incentivizing reinvestment in U.S.-based research and development including software development across a wide range of industries. In this context, accurate and defensible estimation of qualified research expenses (QREs) is of high importance. Statistical sampling provides a practical framework for estimating QREs for a well-defined population of business components (sampling frame) documented in accordance with Internal Revenue Code Section 41 (Form 6765). IRS Revenue Procedure 2011-42 permits both attribute and variable statistical methods, although the latter (stratified mean and difference estimators) are often regarded as the standard approach to QRE estimation. In this paper, we compare attribute and variable statistical methods within a simulation study and demonstrate that attribute methods offer several theoretical and practical advantages. A key concern with the simple attribute estimator is the possibility of upward bias in QRE determination arising from a preponderance of high potential QRE (pQRE), non-qualified projects in the sampling frame. We address this concern by introducing a stratified sampling design that ensures adequate representation of high pQRE projects in the sample. We demonstrate that a convolution-based stratified attribute estimator produces a valid one-sided 95% lower confidence bound on total QREs across a range of sampling frame structures.
Comments27 pages, 6 tables, 1 figure
