When there is uncertainty concerning the appropriate statistical model to use in representing the data sampling process and corresponding estimators, we consider a basis for optimally combining estimation problems. In the context of the multivariate linear statistical model, we consider a...

This paper introduces a new class of estimators based on minimization of the Cressie-Read (CR) power divergence measure for binary choice models, where neither a parameterized distribution nor a parameterization of the mean is specified explicitly in the statistical model. By incorporating...

This paper presents empirical evidence concerning the finite sample performance of conventional and generalized empirical likelihood-type estimators that utilize instruments in the context of linear structural models characterized by endogenous explanatory variables. There are suggestions in the...

This paper makes three principal contributions. First, we propose a new estimator for the unobservable variable models with endogenous causes. We show that under factor analysis type of assumptions, Robinson and Ferrara (1977)'’s procedure is not fully efficient, and a more efficient procedure...

This paper introduces a semi-parametric bootstrapping approach to Bayesian analysis of structural parameters in simultaneous equation systems that extends the single and multivariate regression approaches of Heckelei and Mittelhammer (1996, 2002) to models with endogenous regressors. Monte Carlo...

Finite sampling properties of information theoretic estimators of the simultaneous equations model, including maximum empirical likelihood, maximum empirical exponential likelihood, and maximum log Euclidean likelihood, are examined in the presence of selected forms of heteroskedasticity....

This paper makes three principal contributions. First, we propose a new estimator for the unobservable variable models with endogenous causes. We show that under factor analysis type of assumptions, Robinson and Ferrara (1977)'s procedure is not fully efficient, and a more efficient procedure...

This paper makes two principal contributions: first, we propose a method for representing well-being aggregates and estimating population subgroup decompositions when data is available on population distributions across subgroups; second, we analyze the Quality of Life (QOL) of 'poor' and...

In this paper we illustrate the use of alternative truncated regression estimators for the general linear model. These include variations of maximum likelihood, Bayesian, and maximum entropy estimators in which the error distributions are doubly truncated. To evaluate the performance of the...