Estimation by maximumlikelihood is burdensome for models such that convolutions and stable distributions. Alternatively, we propose to use moments based on the empirical characteristic function. The objective of this paper is to propose an asymptotically efficient estimator.

Testing frequently involves nuisance parameters wich are identified only under the alternative. This article proposes a class of test statistics wich asymptotic distributions are standard, namely chi-squares. We estimate the parameter vector using the generalized method of moments applied to...

This paper studies the asymptotic validity of the regularized Anderson Rubin (AR) tests in linear models with large number of instruments. The regularized AR tests use informationreduction methods to provide robust inference in instrumental variable (IV) estimation for data rich environments. We...