Moment Restriction-Based Econometric Methods: An overview

Abstract

Moment restriction-based econometric modelling is a broad class which includes the parametric, semiparametric and nonparametric approaches. Moments and conditional moments themselves are nonparametric quantities. If a model is specified in part up to some finite dimensional parameters, this will provide semiparametric estimates or tests. If we use the score to construct moment restrictions to estimate finite dimensional parameters, this yields maximum likelihood (ML) estimates. Semiparametric or nonparametric settings based on moment restrictions have been the main concern in the literature, and comprise the most important and interesting topics. The purpose of this special issue on “Moment Restriction-based Econometric Methods” is to highlight some areas in which novel econometric methods have contributed significantly to the analysis of moment restrictions, specifically asymptotic theory for nonparametric regression with spatial data, a control variate method for stationary processes, method of moments estimation and identifiability of semiparametric nonlinear errors-in-variables models, properties of the CUE estimator and a modification with moments, finite sample properties of alternative estimators of coefficients in a structural equation with many instruments, instrumental variable estimation in the presence of many moment conditions, estimation of conditional moment restrictions without assuming parameter identifiability in the implied unconditional moments, moment-based estimation of smooth transition regression models with endogenous variables, a consistent nonparametric test for nonlinear causality, and linear programming-based estimators in simple linear regression.