Recent advances in the econometric modelling of count data have often been based on the generalized method of moments (GMM). However, the two-step GMM procedure may perform poorly in small samples, and several empirical likelihood-based estimators have been suggested alternatively. In this paper I discuss empirical likelihood (EL) estimation for count data models with endogenous regressors. I c...

This study investigates the moderating effect of firm size in the relationship between corporate governance (board size, board independence and ownership concentration) and banks’ risk-taking (insolvency risk and credit risk). Secondary data (annual reports) was collected from a sample of 21 Malaysian commercial banks covering the 2005–2014 accounting period. An empirical model using pooled ord...

This paper shows how to estimate models by the generalized method of moments and the generalized empirical likelihood using the R package gmm. A brief discussion is offered on the theoretical aspects of both methods and the functionality of the package is presented through several examples in economics and finance. It is a modified version of Chaussé (2010) published in the Journal of Statistic...

1 This paper provides a first order asymptotic theory for generalized method of moments (GMM) estimators when the number of moment conditions is allowed to increase with the sample size and the moment conditions may be weak. Examples in which these asymptotics are relevant include instrumental variable (IV) estimation with many (possibly weak or uninformed) instruments and some panel data model...

As machine learning techniques become more popular and computers become capable of storing and processing large quantities of data, there have been many recent efforts to incorporate such techniques into structural econometric models. My research aims to extend this literature by introducing the techniques of regularization and classification (from machine learning) into Generalized Method of M...

We develop a generalized method of moments (GMM) estimator for the distribution of a variable where summary statistics are available only for intervals of the random variable. Without individual data, one cannot calculate the weighting matrix for the GMM estimator. Instead, we propose a simulated weighting matrix based on a first-step consistent estimate. When the functional form of the underly...

This paper essentially extends the generalized spectral estimation of Berkowitz (2001) to provide a consistent generalized spectral estimator (GSE), considering all the information available, possibly with in…nite dimensions, based upon Escanciano (2006). Our estimator can entertain the strengths of the Berkowitz-GSE over the standard GMM. In contrast, more importantly, the newly proposed estim...