Tests with correct size when instruments can be arbitrarily weak

Marcelo J. Moreira ()

Journal of Econometrics, 2009, vol. 152, issue 2, pages 131-140

Abstract: This paper applies classical exponential-family statistical theory to develop a unifying framework for testing structural parameters in the simultaneous equations model under the assumption of normal errors with known reduced-form variance matrix. The results can be divided into the limited-information and full-information categories. In the limited-information model, it is possible to characterize the entire class of similar tests in a model with only one endogenous explanatory variable. In the full-information framework, this paper proposes a family of similar tests for subsets of endogenous variables' coefficients. For both limited- and full-information models, there exist power upper bounds for unbiased tests. When the model is just-identified, the Anderson-Rubin, score, and (pseudo) conditional likelihood ratio tests are optimal. When the model is over-identified, the (pseudo) conditional likelihood ratio test has power close to the power envelope when identification is strong.

Keywords: Instrumental; variables; regression; Curved; exponential; family; Weak; instruments; Pre-testing (search for similar items in EconPapers)
Date: 2009
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Persistent link: http://EconPapers.repec.org/RePEc:eee:econom:v:152:y:2009:i:2:p:131-140

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