Hypothesis Testing with Efficient Method of Moments Estimation

1987/10/01 by Whitney K. Newey, Kenneth D. West · 1,887 citations
Economics, Econometrics and Finance · Mathematics · #Applied mathematics #Econometrics #Economics #Equivalence (formal languages) #Estimating equations #Estimation #Estimation theory #Financial Risk and Volatility Modeling #Generalized least squares #Generalized method of moments #Heteroscedasticity #Lagrange multiplier #Least-squares function approximation #Mathematical optimization #Mathematics #Maximum likelihood #Method of moments (probability theory) #Monetary Policy and Economic Impact #Non-linear least squares #Ordinary least squares #Score test #Statistical Methods and Inference #Statistical hypothesis testing #Statistics #Wald test

paper · doi:10.2307/2526578

published in International Economic Review 28(3), 777 (Wiley)

openalex publication_date 1987/10/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

Efficient method of moments estimation techniques include many commonly used techniques, including ordinary least squares, two- and three-stage least squares, quasi maximum likelihood, and versions of these for nonlinear environments. For models estimated by any efficient method of moments technique, the authors define analogues to the maximum likeliho od based Wald, likelihood ratio, Lagrange multiplier, and minimum chi-squared statistics. They prove the mutual asymptotic equivalence of the four in an environment that allows for disturbances that are auto correlated and heteroskedastic. They also describe a very convenient way to test a linear hypothesis in a linear model. Copyright 1987 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.

Citations

• Large Sample Properties of Generalized Method of Moments Estimators

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