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...

The problem of weak instruments is due to a very small concentration parameter. To boost the concentration parameter, we propose to increase the number of instruments to a large number or even up to a continuum. However, in finite samples, the inclusion of an excessive number of moments may be...

The use of many moment conditions improves the asymptotic efficiency of the instrumental variables estimators. However, in finite samples, the inclusion of an excessive number of moments increases the bias. To solve this problem, we propose regularized versions of the limited information maximum...