Generalized method of moments estimation

Abstract

Generalized Method of Moments (GMM) refers to a class of estimators which are constructed\rfrom exploiting the sample moment counterparts of population moment conditions (sometimes\rknown as orthogonality conditions) of the data generating model. GMM estimators\rhave become widely used, for the following reasons:\r• GMM estimators have large sample properties that are easy to characterize in ways\rthat facilitate comparison. A family of such estimators can be studied a priori in ways\rthat make asymptotic efficiency comparisons easy. The method also provides a natural\rway to construct tests which take account of both sampling and estimation error.\r• In practice, researchers find it useful that GMM estimators can be constructed without\rspecifying the full data generating process (which would be required to write down the\rmaximum likelihood estimator.) This characteristic has been exploited in analyzing\rpartially specified economic models, in studying potentially misspecified dynamic models\rdesigned to match target moments, and in constructing stochastic discount factor\rmodels that link asset pricing to sources of macroeconomic risk.\r