Add to ORKGWe study a parametric estimation problem related to moment condition models. As an alternative to the generalized empirical likelihood (GEL) and the generalized method of moments (GMM), a Bayesian approach to the problem can be adopted, extending the MEM procedure to parametric moment conditions. We show in particular that a large number of GEL estimators can be interpreted as a maximum entropy solution. Moreover, we provide a more general field of applications by proving the method to be robust to approximate moment conditions
I study a semiparametric Bayesian method for over-identified moment condition models. A mixture of p...
The maximum entropy principle (MEP) is a powerful statistical inference tool that provides a rigorou...
Procedures based on the Generalized Method of Moments (GMM) are basic tools in modern econometrics. ...
We study a parametric estimation problem related to moment condition models. As an alternative to th...
Bayesian inference in moment condition models is difficult to implement. For these models, a posteri...
The method of maximum entropy is quite a powerful tool to solve the generalized moment problem, whic...
This paper studies the properties of generalised empirical likelihood (GEL) methods for the estimati...
Zellner has proposed a novel methodology for estimating structural parameters and predicting future ...
The recovering of a discrete probability distribution taking on a countable values, when only partia...
The aim of this thesis is to investigate Generalised Empirical Likelihood (GEL) and related informat...
In this letter, we elaborate on some of the issues raised by a recent paper by Neapolitan and Jiang ...
Using many moment conditions can improve efficiency but makes the usual generalized method of moment...
In many practical situations, we have only partial information about the probabilities. In some case...
The recovering of a positive density function of which a finite number of moments are assigned is co...
I study a semiparametric Bayesian method for over-identified moment condition models. A mixture of p...
I study a semiparametric Bayesian method for over-identified moment condition models. A mixture of p...
The maximum entropy principle (MEP) is a powerful statistical inference tool that provides a rigorou...
Procedures based on the Generalized Method of Moments (GMM) are basic tools in modern econometrics. ...
We study a parametric estimation problem related to moment condition models. As an alternative to th...
Bayesian inference in moment condition models is difficult to implement. For these models, a posteri...
The method of maximum entropy is quite a powerful tool to solve the generalized moment problem, whic...
This paper studies the properties of generalised empirical likelihood (GEL) methods for the estimati...
Zellner has proposed a novel methodology for estimating structural parameters and predicting future ...
The recovering of a discrete probability distribution taking on a countable values, when only partia...
The aim of this thesis is to investigate Generalised Empirical Likelihood (GEL) and related informat...
In this letter, we elaborate on some of the issues raised by a recent paper by Neapolitan and Jiang ...
Using many moment conditions can improve efficiency but makes the usual generalized method of moment...
In many practical situations, we have only partial information about the probabilities. In some case...
The recovering of a positive density function of which a finite number of moments are assigned is co...
I study a semiparametric Bayesian method for over-identified moment condition models. A mixture of p...
I study a semiparametric Bayesian method for over-identified moment condition models. A mixture of p...
The maximum entropy principle (MEP) is a powerful statistical inference tool that provides a rigorou...
Procedures based on the Generalized Method of Moments (GMM) are basic tools in modern econometrics. ...