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来源类型Working Paper
规范类型报告
DOI10.3386/w21875
来源IDWorking Paper 21875
Multiple Hypothesis Testing in Experimental Economics
John A. List; Azeem M. Shaikh; Yang Xu
发表日期2016-01-18
出版年2016
语种英语
摘要Empiricism in the sciences allows us to test theories, formulate optimal policies, and learn how the world works. In this manner, it is critical that our empirical work provides accurate conclusions about underlying data patterns. False positives represent an especially important problem, as vast public and private resources can be misguided if we base decisions on false discovery. This study explores one especially pernicious influence on false positives—multiple hypothesis testing (MHT). While MHT potentially affects all types of empirical work, we consider three common scenarios where MHT influences inference within experimental economics: jointly identifying treatment effects for a set of outcomes, estimating heterogeneous treatment effects through subgroup analysis, and conducting hypothesis testing for multiple treatment conditions. Building upon the work of Romano and Wolf (2010), we present a correction procedure that incorporates the three scenarios, and illustrate the improvement in power by comparing our results with those obtained by the classic studies due to Bonferroni (1935) and Holm (1979). Importantly, under weak assumptions, our testing procedure asymptotically controls the familywise error rate – the probability of one false rejection – and is asymptotically balanced. We showcase our approach by revisiting the data reported in Karlan and List (2007), to deepen our understanding of why people give to charitable causes.
主题Econometrics ; Estimation Methods ; Experimental Design
URLhttps://www.nber.org/papers/w21875
来源智库National Bureau of Economic Research (United States)
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资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/579550
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GB/T 7714
John A. List,Azeem M. Shaikh,Yang Xu. Multiple Hypothesis Testing in Experimental Economics. 2016.
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