Recent Developments in Inference: Practicalities for Applied Economics
统计推断的近期进展：应用经济学实践AI 解读 ↓
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统计推断的近期进展：应用经济学实践
- 研究问题
- 面对异方差、聚类、序列相关和多重检验等问题，应用经济学研究应如何选择标准误、检验统计量及数值推断方法？
- 方法与思路
- 作者综述常规推断面临的挑战与修正方法，讨论自助法、野生自助法和随机化推断，并介绍相关 Stata 命令。
- 材料陈述的贡献
- 摘要提出明确数据中的推断挑战、正确计算标准误与检验量，并建议借助自助法获得渐近修正；这些属于作者的实践建议。
- 阅读关注点
- 核对各种方法对应的抽样和研究设计条件、重抽样单位及聚类层级，检查自助法建议的适用范围和命令实现细节。
- 阅读边界
- 所据为 arXiv 预印本，不能确认同行评审或期刊接收；摘要未提供各方法的条件与例外，不能理解为自助法普遍有效。
原文摘要
We provide a review of recent developments in the calculation of standard errors and test statistics for statistical inference. While much of the focus of the last two decades in economics has been on generating unbiased coefficients, recent years has seen a variety of advancements in correcting for non-standard standard errors. We synthesize these recent advances in addressing challenges to conventional inference, like heteroskedasticity, clustering, serial correlation, and testing multiple hypotheses. We also discuss recent advancements in numerical methods, such as the bootstrap, wild bootstrap, and randomization inference. We make three specific recommendations. First, applied economists need to clearly articulate the challenges to statistical inference that are present in data as well as the source of those challenges. Second, modern computing power and statistical software means that applied economists have no excuse for not correctly calculating their standard errors and test statistics. Third, because complicated sampling strategies and research designs make it difficult to work out the correct formula for standard errors and test statistics, we believe that in the applied economics profession it should become standard practice to rely on asymptotic refinements to the distribution of an estimator or test statistic via bootstrapping. Throughout, we reference built-in and user-written Stata commands that allow one to quickly calculate accurate standard errors and relevant test statistics.