Ogundimu, Emmanuel O. and Hutton, Jane L. (2016) 'A Sample Selection Model with Skew-normal Distribution.', Scandinavian Journal of Statistics, 43 (1). pp. 172-190.
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.
|Full text:||(AM) Accepted Manuscript|
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|Publisher Web site:||https://doi.org/10.1111/sjos.12171|
|Publisher statement:||This is the peer reviewed version of the following article: Ogundimu, Emmanuel O. & Hutton, Jane L. (2016). A Sample Selection Model with Skew-normal Distribution. Scandinavian Journal of Statistics 43(1): 172-190., which has been published in final form at https://doi.org/10.1111/sjos.12171. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.|
|Date accepted:||25 July 2015|
|Date deposited:||15 October 2021|
|Date of first online publication:||30 July 2015|
|Date first made open access:||15 October 2021|
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