When Good Balance Goes Bad: A Discussion of Common Pitfalls When Using Entropy Balancing
讨论了熵平衡法在会计实证研究中的常见问题，指出在面板数据中该方法对控制样本和研究设计的微小变化敏感，并以审计费用溢价为例提出解决方案。
ABSTRACT For many accounting research questions, empirical researchers cannot randomly assign observations to treatment conditions or identify a quasi-experimental setting. In these cases, entropy balancing (Hainmueller 2012) is an increasingly popular statistical method for identifying a control sample that is nearly identical to the treated sample with respect to observable covariates. In this paper, we compare entropy balancing's approach of reweighting control sample observations to ordinary least squares and propensity score matching. We demonstrate that researchers applying entropy balancing in empirical settings involving panel data with features common in accounting research may encounter implementation issues that render the resulting estimates sensitive to relatively minor changes in the control sample or the research design. Using the setting of estimating the Big-N audit fee premium, we empirically demonstrate these issues and propose solutions. Data Availability: Data are available from public sources cited in the text. JEL Classifications: C18; M4.