Abstract: This paper investigates the family of empirical Cressie-Read discrepancy statistics with moment restric-tions, which is a generalization of Baggerly(1998). It is shown that the statistics in the family are all st-order equivalent to empirical likelihood under suitable conditions, and hence, they are all asymptotically 2 distributed and the corresponding estimators of the parameters are all asymptotically normal. The rst-order eciency, using conventional Pitman approach based on the comparison of local power, of these statistics is veried. Furthermore, It is proved that the empirical likelihood ratio is the unique member of the family to be locally unbiased, which is consistent with the results of Bravo (2003). References [1] Bagge...
This paper proposes an asymptotically efficient method for estimating models with conditional moment...
We construct two classes of smoothed empirical likelihood ratio tests for the conditional inde-pende...
This paper proposes an asymptotically efficient method for estimating models with conditional moment...
We consider a very general class of empirical discrepancy statistics that includes the Cressie-Read ...
In this paper, we generalize the results obtained with the Kullbackdistance (corresponding to empiri...
This thesis identifies the asymptotic properties of generalized empirical likelihood estimators when...
The aim of this thesis is to investigate Generalised Empirical Likelihood (GEL) and related informat...
The empirical likelihood (EL) technique is a powerful nonparametric method with wide theoretical and...
This thesis consists of three research chapters on the theory of empirical likelihood (EL), which is...
An empirical likelihood test is proposed for parameters of models defined by conditional moment rest...
Empirical likelihood is an estimation method inspired by the classical likelihood method, but withou...
Abstract Suppose that several different imperfect instruments and one perfect instru-ment are indepe...
The following material is focused on the statistical inference of moment structure models. Although ...
Models defined by moment conditions are at the center of structural econometric estimation, but econ...
Chapter 1 is a non technical introduction to the thesis. In chapter 2, Basics of Large Deviation The...
This paper proposes an asymptotically efficient method for estimating models with conditional moment...
We construct two classes of smoothed empirical likelihood ratio tests for the conditional inde-pende...
This paper proposes an asymptotically efficient method for estimating models with conditional moment...
We consider a very general class of empirical discrepancy statistics that includes the Cressie-Read ...
In this paper, we generalize the results obtained with the Kullbackdistance (corresponding to empiri...
This thesis identifies the asymptotic properties of generalized empirical likelihood estimators when...
The aim of this thesis is to investigate Generalised Empirical Likelihood (GEL) and related informat...
The empirical likelihood (EL) technique is a powerful nonparametric method with wide theoretical and...
This thesis consists of three research chapters on the theory of empirical likelihood (EL), which is...
An empirical likelihood test is proposed for parameters of models defined by conditional moment rest...
Empirical likelihood is an estimation method inspired by the classical likelihood method, but withou...
Abstract Suppose that several different imperfect instruments and one perfect instru-ment are indepe...
The following material is focused on the statistical inference of moment structure models. Although ...
Models defined by moment conditions are at the center of structural econometric estimation, but econ...
Chapter 1 is a non technical introduction to the thesis. In chapter 2, Basics of Large Deviation The...
This paper proposes an asymptotically efficient method for estimating models with conditional moment...
We construct two classes of smoothed empirical likelihood ratio tests for the conditional inde-pende...
This paper proposes an asymptotically efficient method for estimating models with conditional moment...