Hypothesis Testing in Econometrics
Joseph P. Romano
Stanford University - Department of Statistics
University of Chicago
University of Zurich - Department of Economics Library; University of Zurich - Department of Eonomics
Annual Review of Economics, Vol. 2, pp. 75-104, 2010
This article reviews important concepts and methods that are useful for hypothesis testing. First, we discuss the Neyman-Pearson framework. Various approaches to optimality are presented, including finite-sample and large-sample optimality. Then, we summarize some of the most important methods, as well as resampling methodology, which is useful to set critical values. Finally, we consider the problem of multiple testing, which has witnessed a burgeoning literature in recent years. Along the way, we incorporate some examples that are current in the econometrics literature. While many problems with well-known successful solutions are included, we also address open problems that are not easily handled with current technology, stemming from such issues as lack of optimality or poor asymptotic approximations.
Accepted Paper Series
Date posted: October 18, 2010
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