Tests with correct size when instruments can be arbitrarily weak

Resumo

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. (C) 2009 Elsevier B.V. All rights reserved.

Ficha do documento

Tipo

Artigo científico

Ano

2009

Instituição

Elsevier Science Sa

Fonte

Repositório da FGV

Idioma

Inglês

Acesso

Acesso restrito

DOI

10.1016/j.jeconom.2009.01.012

Identificador

oai:repositorio.fgv.br:10438/23144

Temas

EconomiaDados

Palavras-chave

Instrumental variables regressionCurved exponential familyWeak instrumentsPre-testingEconomiaVariáveis instrumentais (Estatística)