交互模型中省略的变量偏差:一个警示故事

IF 7.6 1区 经济学 Q1 ECONOMICS
B. Feigenberg, Ben Ost, Javaeria Qureshi
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引用次数: 0

摘要

我们强调,使用交互作用项来研究治疗效果异质性的分析容易受到一种在经济学中经常被忽视的遗漏变量偏差的影响。与大多数遗漏变量偏差的情况不同,本例中的遗漏变量可供研究人员使用,但未包含在模型中。我们根据《美国经济评论》在五年内发表的17篇论文中的205项估计结果证明,这种排除很重要。在这些论文中,大约60%的论文没有考虑到遗漏的变量,导致大多数估计值的变化超过100%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Omitted Variable Bias in Interacted Models: A Cautionary Tale
We highlight that analyses using interaction terms to study treatment effect heterogeneity are susceptible to a form of omitted variable bias that is often overlooked in economics. Unlike most instances of omitted variable bias, the omitted variables in this case are available to the researcher but were not included in the model. We demonstrate that this exclusion matters based on a replication of 205 estimates across 17 papers published in the American Economic Review over a five-year period. For approximately 60% of these papers, failing to account for the omitted variables changes the majority of estimates by more than 100%.
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来源期刊
CiteScore
8.50
自引率
0.00%
发文量
175
期刊介绍: The Review of Economics and Statistics is a 100-year-old general journal of applied (especially quantitative) economics. Edited at the Harvard Kennedy School, the Review has published some of the most important articles in empirical economics.
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