T he method of moments approach to parameter estimation dates back more than 100 years (Stigler, 1986). The notion of a moment is fundamental for describing features of a population. For example, the population mean (or population average), usually denoted m, is the moment that measures central tendency. If y is a random variable describing the population of interest, we also write the populati...

Generalized method of moments (GMM) estimation has become an important unifying framework for inference in econometrics in the last 20 years. It can be thought of as encompassing almost all of the common estimation methods, such as maximum likelihood, ordinary least squares, instrumental variables, and two-stage least squares, and nowadays is an important part of all advanced econometrics textb...

The contribution of generalized method of moments (Hansen and Singleton, 1982) was to allow frequentist inference regarding the parameters of a nonlinear structural model without having to solve the model. Provided there were no latent variables. The contribution of this paper is the same. With latent variables.

In this paper we propose a class of efficient Generalized Method-of-Moments (GMM) algorithms for computing parameters of the Plackett-Luce model, where the data consists of full rankings over alternatives. Our technique is based on breaking the full rankings into pairwise comparisons, and then computing parameters that satisfy a set of generalized moment conditions. We identify conditions for t...

Part A reviews the basic estimation theory of the generalized method of moments (GMM) and Part B deals with optimal instrumental variables. 1 For the most part, we restrict attention to iid observations. Linear Regression Economists often use linear regression to quantify a relationship between economic variables. A linear regression between y and x is a relationship of the form y = x 0 β + ε (...

11 THE GMM ESTIMATION 2 11.1 Consistency and Asymptotic Normality . . . . . . . . . . . . . . . . . . . . . 3 11.2 Regularity Conditions and Identification . . . . . . . . . . . . . . . . . . . . . 4 11.3 The GMM Interpretation of the OLS Estimation . . . . . . . . . . . . . . . . . 5 11.4 The GMM Interpretation of the MLE . . . . . . . . . . . . . . . . . . . . . . . 6 11.5 The GMM Estimation ...

The Robust Robust Generalized Methods of Moments (RGMM) and the Indirect Robust GMM (IRGMM) are algorithms for estimating parameter values in statistical models, such as diffusion models for interest rates, in a robust way. The long computation time is one of the main challenge facing these methods. In this paper, we introduce accelerated variants of RGMM and IRGMM. The fixed point iteration in...