gnbstrat fits a maximum-likelihood generalized negative binomial with endogenous stratification regression model of depvar on indepvars, where depvar is a nonnegative count variable > 0. lnalpha is parameterized by the predictors entered within its parentheses. gnbstrat simultaneously accommodates three features of on-site samples dealing with count data: overdispersion relative to the Poisson; truncation at zero, and endogenous stratification due to oversampling of frequent users of the site. Endogenous stratification occurs when the likelihood of sampling observations is dependent on a choice made by the subject of study which is in itself the dependent variable. For example, in recreational demand analysis, if an on-site survey is conduc...
The Generalized Pareto-Negative Binomial (GP-NB) model was introduced to find the connections betwee...
This paper discusses the specification and estimation of seemingly unrelated multivariate count data...
Negative binomial regression model is used to overcome the overdispersion in Poisson regression mode...
The generalized method of moments (GMM) estimation technique is discussed for count data models with...
We present new Stata commands for estimating several regression models suitable for analyzing overdi...
We describe specification and estimation of a multinomial treatment effects negative binomial regres...
We describe specification and estimation of a multinomial treatment effects negative binomial regres...
We present motivation and new Stata commands for modeling count data. While the focus of this articl...
The Generalized Pareto-Negative Binomial (GP-NB) model was introduced to find the connections betwee...
We include Stata syntax that creates panel datasets for negative binomial time series regression ana...
The generalized negative binomial distribution characterized by three parameters, has been used to f...
The Generalized Pareto-Negative Binomial (GP-NB) model was introduced to find the connections betwee...
The generalized negative binomial distribution characterized by three parameters, has been used to f...
In this paper, we propose a generalized likelihood ratio test to discernwhether a set of data fits a...
The generalized negative binomial distribution characterized by three parameters, has been used to f...
The Generalized Pareto-Negative Binomial (GP-NB) model was introduced to find the connections betwee...
This paper discusses the specification and estimation of seemingly unrelated multivariate count data...
Negative binomial regression model is used to overcome the overdispersion in Poisson regression mode...
The generalized method of moments (GMM) estimation technique is discussed for count data models with...
We present new Stata commands for estimating several regression models suitable for analyzing overdi...
We describe specification and estimation of a multinomial treatment effects negative binomial regres...
We describe specification and estimation of a multinomial treatment effects negative binomial regres...
We present motivation and new Stata commands for modeling count data. While the focus of this articl...
The Generalized Pareto-Negative Binomial (GP-NB) model was introduced to find the connections betwee...
We include Stata syntax that creates panel datasets for negative binomial time series regression ana...
The generalized negative binomial distribution characterized by three parameters, has been used to f...
The Generalized Pareto-Negative Binomial (GP-NB) model was introduced to find the connections betwee...
The generalized negative binomial distribution characterized by three parameters, has been used to f...
In this paper, we propose a generalized likelihood ratio test to discernwhether a set of data fits a...
The generalized negative binomial distribution characterized by three parameters, has been used to f...
The Generalized Pareto-Negative Binomial (GP-NB) model was introduced to find the connections betwee...
This paper discusses the specification and estimation of seemingly unrelated multivariate count data...
Negative binomial regression model is used to overcome the overdispersion in Poisson regression mode...