This entry describes the basic framework for statistical estimation and inference using Generalized Method of Moments and also illustrates the types of empirical models in finance to which these techniques have been applied. Generalized Method of Moments (GMM) provides a computationally convenient method of obtaining consistent and asymptotically normally distributed estimators of the parameters of statistical models. The method has been applied in many areas of economics but has arguably been most frequently applied in finance. In fact, it was in empirical finance that the power of the method was first illustrated: for while Hansen [7] introduced GMM and presented its fundamental statistical theory, Hansen and Hodrick [9] and Hansen and Singleton [10] showed the potential of the GMM approach to estimation through their empirical analyses of, respectively, foreign exchange markets and asset pricing. This entry briefly describes the GMM framework for estimation and inference in the case where time series data are used. The reader is referred to [6] for a comprehensive treatment of GMM that presents both a rigorous statistical analysis of the method and empirical illustrations in finance. For applications of GMM to panel data, see [15].

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