A unified approach to multilevel sample selection models

Ogundimu, Emmanuel O.; Hutton, Jane L.

doi:10.1080/03610926.2014.887108

A unified approach to multilevel sample selection models

Ogundimu, Emmanuel O.; Hutton, Jane L.

Authors

Dr Emmanuel Ogundimu emmanuel.ogundimu@durham.ac.uk
Associate Professor

Jane L. Hutton

Abstract

We propose a unified approach for multilevel sample selection models using a generalized result on skew distributions arising from selection. If the underlying distributional assumption is normal, then the resulting density for the outcome is the continuous component of the sample selection density and has links with the closed skew-normal distribution (CSN). The CSN distribution provides a framework which simplifies the derivation of the conditional expectation of the observed data. This generalizes the Heckman’s two-step method to a multilevel sample selection model. Finite-sample performance of the maximum likelihood estimator of this model is studied through a Monte Carlo simulation.

Citation

Ogundimu, E. O., & Hutton, J. L. (2016). A unified approach to multilevel sample selection models. Communications in Statistics - Theory and Methods, 45(9), 2592-2611. https://doi.org/10.1080/03610926.2014.887108

Journal Article Type	Article
Acceptance Date	Jun 21, 2014
Online Publication Date	Apr 6, 2016
Publication Date	2016
Deposit Date	Oct 11, 2020
Publicly Available Date	Oct 15, 2021
Journal	Communications in Statistics - Theory and Methods
Print ISSN	0361-0926
Electronic ISSN	1532-415X
Publisher	Taylor and Francis Group
Peer Reviewed	Peer Reviewed
Volume	45
Issue	9
Pages	2592-2611
DOI	https://doi.org/10.1080/03610926.2014.887108

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Copyright Statement
This is an Accepted Manuscript version of the following article, accepted for publication in Communications in Statistics - Theory and Methods. Ogundimu, Emmanuel O. & Hutton, Jane L. (2016). A unified approach to multilevel sample selection models. Communications in Statistics - Theory and Methods 45(9): 2592.. It is deposited under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited.