INFERRING ANTICOMPETITIVE PRICE EFFECTS FROM DIFFERENCE-IN-DIFFERENCE ANALYSIS: A CAVEAT

Shawn W. Ulrick, Seth B. Sacher
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引用次数: 5

Abstract

Difference-in-differences, or “D-in-D,” is perhaps the most broadly applied econometric technique in retrospective analyses of competition matters. We discuss a possible pitfall regarding this procedure. We argue that a positive and significant event variable coefficient is not a sufficient condition for concluding that there have been anticompetitive price effects. We use simulations to demonstrate that even in cases where the alleged anticompetitive activity had no anticompetitive effect, the D-in-D procedure can still produce positive and significant event variables. This article does not take issue with D-in-D in principle but rather as it is often practiced. Our results imply that while D-in-D is an important tool, the researcher must conduct additional analyses to put the D-in-D result into context before concluding a significant event variable is indicative of anticompetitive effects. We suggest a specific approach. We note that our results may have important implications for the current state of the academic literature regarding retrospectives in antitrust as well as for practitioners.
从差异中差异分析推断反竞争价格效应:警告
差异中的差异,或“D-in-D”,可能是在竞争问题的回顾性分析中应用最广泛的计量经济学技术。我们讨论了关于这个过程的一个可能的陷阱。我们认为一个正且显著的事件变量系数并不是得出存在反竞争价格效应的充分条件。我们使用模拟来证明,即使在所谓的反竞争活动没有反竞争效果的情况下,D-in-D程序仍然可以产生积极和显著的事件变量。本文在原则上不反对D-in-D,而是通常的实践。我们的研究结果表明,虽然D-in-D是一个重要的工具,但研究者必须进行额外的分析,将D-in-D结果置于上下文中,然后才能得出一个重要的事件变量是反竞争效应的指示。我们建议一个具体的方法。我们注意到,我们的结果可能对反垄断回溯的学术文献现状以及从业者具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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