 Semi-parametric copula sample selection models for count responsesGiampiero Marra and Karol Wyszynski Computational Statistics & Data Analysis, 2016, vol. 104, issue C, 110-129 Abstract: In observational studies, a response of interest (as well as some individual level characteristics) may be observed for a non-randomly selected sample of the population. In this situation, standard models such as linear and probit regressions will yield biased and inconsistent parameter estimates. Selection models can address this issue and mainly consist of two regressions: a binary selection equation which determines whether the statistical units will enter the sample, and an outcome equation which models the response. While sample selection models for continuous and binary outcomes have been widely studied in the literature, the case of count response has not received as much attention. Sample selection models for count data which allow for the use of potentially any discrete distribution, non-Gaussian dependencies between the selection and outcome equations, and flexible covariate effects are introduced. The estimation algorithm is based on the penalized likelihood estimation framework. The method is illustrated in simulation and using data from a United States Veterans Administration Survey. Keywords: Non-random sample selection; Copula; Penalized regression spline; Count response (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:104:y:2016:i:c:p:110-129 DOI: 10.1016/j.csda.2016.06.003 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.