GMMestimatorer

GMMestimatorer is a term that refers to estimators within the framework of Generalized Method of Moments (GMM) estimation. GMM is a powerful and flexible econometric technique used to estimate parameters of statistical models when the underlying distribution of the data is unknown or complex. It is particularly useful in situations where direct maximum likelihood estimation is difficult or impossible.

The core idea behind GMM is to use a set of moment conditions that are expected to hold true for the true parameter values. These moment conditions are typically derived from the theoretical model being considered. The GMM estimator then finds the parameter values that make the sample analogues of these moment conditions as close to zero as possible. This is achieved by minimizing a quadratic form of the sample moment conditions, where the quadratic form is weighted by a weighting matrix.

The choice of weighting matrix is crucial in GMM. An optimal weighting matrix, typically the inverse of the asymptotic covariance matrix of the moment conditions, leads to the most efficient estimator. However, in practice, the optimal weighting matrix is often unknown and must be estimated. This leads to a class of GMM estimators, including one-step, two-step, and iterated GMM estimators, each with different properties regarding efficiency and computational complexity.

GMM estimators are widely used in various fields, including finance, macroeconomics, and labor economics, for estimating parameters in models with endogeneity, dynamic panel data, and limited dependent variables. Their robustness to misspecification of the data distribution and their ability to handle a wide range of models make them a valuable tool for empirical researchers.