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ENDOGENEITY IN COUNT DATA MODELS: AN APPLICATION TO DEMAND FOR HEALTH CARE (r...
The generalized method of moments (GMM) estimation technique is discussed for count data models with endogenous regressors. Count data models can be specified with additive or... -
HETEROGENEITY, EXCESS ZEROS, AND THE STRUCTURE OF COUNT DATA MODELS (replicat...
This paper demonstrates that the unobserved heterogeneity commonly assumed to be the source of overdispersion in count data models has predictable implications for the... -
SEMI-PARAMETRIC ESTIMATION OF HURDLE REGRESSION MODELS WITH AN APPLICATION TO...
This paper develops a semi-parametric estimation method for hurdle (two-part) count regression models. The approach in each stage is based on Laguerre series expansion for the... -
ECONOMIC INCENTIVES AND HOSPITALIZATION IN GERMANY (replication data)
The dramatically rising health expenditures have become a matter of prime concern. Using a rich panel dataset this paper contributes to this debate by investigating factors... -
DEMAND FOR MEDICAL CARE BY THE ELDERLY: A FINITE MIXTURE APPROACH (replicatio...
In this article we develop a finite mixture negative binomial count model that accommodates unobserved heterogeneity in an intuitive and analytically tractable manner. This... -
ESTIMATING THE INNOVATION FUNCTION FROM PATENT NUMBERS: GMM ON COUNT PANEL DA...
The purpose of this paper is to estimate the patent equation, an empirical counterpart to the knowledge-production function. Innovation output is measured through the number of... -
PATENTS, R&D, AND TECHNOLOGICAL SPILLOVERS AT THE FIRM LEVEL: SOME EVIDEN...
This paper analyses the relationship between the main determinants of technological activity and patent applications. To this end, an original panel of 181 international... -
COUNT DATA REGRESSION USING SERIES EXPANSIONS: WITH APPLICATIONS (replication...
A new class of parametric regression models for both under? and overdispersed count data is proposed. These models are based on squared polynomial expansions around a Poisson...
