Hypothesis testing in econometrics

Joseph P. Romano, Azeem Shaikh and Michael Wolf

No 444, IEW - Working Papers from Institute for Empirical Research in Economics - University of Zurich

Abstract: This paper 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, some of the most important methods are summarized, 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 we include many problems with wellknown successful solutions, we also include open problems that are not easily handled with current technology, stemming from issues like lack of optimality or poor asymptotic approximations.

Keywords: Asymptotics; multiple testing; optimality; resampling (search for similar items in EconPapers)
JEL-codes: C12 (search for similar items in EconPapers)
Date: 2009-09
New Economics Papers: this item is included in nep-ecm and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Related works:
Journal Article: Hypothesis Testing in Econometrics (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:zur:iewwpx:444

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