Modeling household fertility decisions: estimation and testing of censored regression models for count data.

Abstract

"This paper adds to the recent body of research on fertility by estimating and testing censored Poisson regression models and censored negative binomial regression models of household fertility decisions. A novel feature of this study is that in each case the censoring threshold varies from individual to individual. Also, a Lagrange multiplier or score test is used to investigate overdispersion. In these regression models the dependent variable is the number of children. In this situation, censored Poisson regression models and censored negative binomial regression models have statistical advantages over OLS, uncensored Poisson regression models, and uncensored negative binomial regression models. The censored models employed in this study are estimated using panel data collected from the Consumer Expenditure Survey compiled by the [U.S.] Bureau of Labor Statistics."