A Sample Selection Model with Skew-normal Distribution

Ogundimu, Emmanuel O.; Hutton, Jane L.

Abstract

Non-random sampling is a source of bias in empirical research. It is common for the outcomes of interest (e.g. wage distribution) to be skewed in the source population. Sometimes, the outcomes are further subjected to sample selection, which is a type of missing data, resulting in partial observability. Thus, methods based on complete cases for skew data are inadequate for the analysis of such data and a general sample selection model is required. Heckman proposed a full maximum likelihood estimation method under the normality assumption for sample selection problems, and parametric and non-parametric extensions have been proposed. We generalize Heckman selection model to allow for underlying skew-normal distributions. Finite-sample performance of the maximum likelihood estimator of the model is studied via simulation. Applications illustrate the strength of the model in capturing spurious skewness in bounded scores, and in modelling data where logarithm transformation could not mitigate the effect of inherent skewness in the outcome variable.

Citation

Ogundimu, E. O., & Hutton, J. L. (2016). A Sample Selection Model with Skew-normal Distribution. Scandinavian Journal of Statistics, 43(1), 172-190. https://doi.org/10.1111/sjos.12171

• Journal Article Type: Article
• Acceptance Date: Jul 25, 2015
• Online Publication Date: Jul 30, 2015
• Publication Date: 2016-03
• Deposit Date: Oct 11, 2020
• Publicly Available Date: Oct 15, 2021
• Journal: Scandinavian Journal of Statistics
• Print ISSN: 0303-6898
• Electronic ISSN: 1467-9469
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 43
• Issue: 1
• Pages: 172-190
• DOI: https://doi.org/10.1111/sjos.12171
• Public URL: https://durham-repository.worktribe.com/output/1254345