In this article, we propose to estimate the regression parameters in a semiparametric generalized linear model by moment estimating equations. These estimators are shown to be consistent and asymptotically normal. We present two estimators of the nonparametric part, provide conditions for the existence and uniform consistency, and obtain faster rates of convergence under weaker assumptions
The GMM estimator is widely used in the econometrics literature. This thesis mainly focus on three a...
We suggest using a class of semiparametric dynamic panel data models to capture individual variation...
Abstract. In this paper, we consider a partial linear regression model with measurement errors in po...
<p>This paper develops the asymptotic theory for the estimation of smooth semiparametric generalized...
In this article, estimation methods of the semiparametric generalized linear model known as the gene...
This paper proposes an empirical likelihood-based estimation method for semiparametric conditional m...
This article introduces a semiparametric extension of generalized linear models that is based on a f...
In econometrics, models stated as conditional moment restrictions are typically estimated by means o...
AbstractAsymptotic expansions are made for the distributions of the Maximum Empirical Likelihood (ME...
Since L. P. Hansen's (1982) seminal paper, the generalized method of moments (GMM) has become an inc...
The generalized method of moments (GMM) is the centrepiece of semiparametric estimation frameworks. ...
Asymptotic expansions are made for the distributions of the Maximum Empirical Likelihood (MEL) estim...
We consider marginal semiparametric partially linear models for longitudinal/clustered data, where t...
The efficiency of the generalized method of moment (GMM) estimator is addressed by using a character...
Partial linear model, NA random variables, Least-squares estimator, Weighted least-squares estimator...
The GMM estimator is widely used in the econometrics literature. This thesis mainly focus on three a...
We suggest using a class of semiparametric dynamic panel data models to capture individual variation...
Abstract. In this paper, we consider a partial linear regression model with measurement errors in po...
<p>This paper develops the asymptotic theory for the estimation of smooth semiparametric generalized...
In this article, estimation methods of the semiparametric generalized linear model known as the gene...
This paper proposes an empirical likelihood-based estimation method for semiparametric conditional m...
This article introduces a semiparametric extension of generalized linear models that is based on a f...
In econometrics, models stated as conditional moment restrictions are typically estimated by means o...
AbstractAsymptotic expansions are made for the distributions of the Maximum Empirical Likelihood (ME...
Since L. P. Hansen's (1982) seminal paper, the generalized method of moments (GMM) has become an inc...
The generalized method of moments (GMM) is the centrepiece of semiparametric estimation frameworks. ...
Asymptotic expansions are made for the distributions of the Maximum Empirical Likelihood (MEL) estim...
We consider marginal semiparametric partially linear models for longitudinal/clustered data, where t...
The efficiency of the generalized method of moment (GMM) estimator is addressed by using a character...
Partial linear model, NA random variables, Least-squares estimator, Weighted least-squares estimator...
The GMM estimator is widely used in the econometrics literature. This thesis mainly focus on three a...
We suggest using a class of semiparametric dynamic panel data models to capture individual variation...
Abstract. In this paper, we consider a partial linear regression model with measurement errors in po...