dedup_wf_001--5616457cd6557bbe26d978211488ac58

The sample selection bias problem occurs when the outcome of interest is only observed according to some selection rule, where there is a dependence structure between the outcome and the selection rule. In a pioneering work, J. Heckman proposed a sample selection model based on a bivariate normal distribution for dealing with this problem. Due to the non-robustness of the normal distribution, many alternatives have been introduced in the literature by assuming extensions of the normal distribution like the Student-t and skew-normal models. One common limitation of the existent sample selection models is that they require a transformation of the outcome of interest, which is common R+-valued, such as income and wage. With this, data are analyzed on a non-original scale which complicates the interpretation of the parameters. In this paper, we propose a sample selection model based on the bivariate Birnbaum–Saunders distribution, which has the same number of parameters that the classical Heckman model. Further, our associated outcome equation is R+-valued. We discuss estimation by maximum likelihood and present some Monte Carlo simulation studies. An empirical application to the ambulatory expenditures data from the 2001 Medical Expenditure Panel Survey is presented.

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PID https://www.doi.org/10.6084/m9.figshare.12848113.v1
PID https://www.doi.org/10.1080/02664763.2020.1780570
PID https://www.doi.org/10.6084/m9.figshare.12848113
URL https://www.scilit.net/article/bd97c89eedb058ec0fa30b9ac7559dc6
URL https://www.tandfonline.com/doi/full/10.1080/02664763.2020.1780570
URL http://dx.doi.org/10.6084/m9.figshare.12848113.v1
URL https://www.tandfonline.com/doi/pdf/10.1080/02664763.2020.1780570
URL https://academic.microsoft.com/#/detail/3035550622
URL http://dx.doi.org/10.6084/m9.figshare.12848113
URL http://dx.doi.org/10.1080/02664763.2020.1780570
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Author Fernando de Souza Bastos, 0000-0003-1503-4599
Author Wagner Barreto-Souza, 0000-0003-0831-7881
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Collected From figshare; Datacite; Crossref; Microsoft Academic Graph
Hosted By Journal of Applied Statistics; figshare
Publication Date 2020-01-01
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Language UNKNOWN
Resource Type Other literature type; Article
keyword keywords.Statistics, Probability and Uncertainty
keyword FOS: Biological sciences
keyword FOS: Mathematics
system:type publication
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Source https://science-innovation-policy.openaire.eu/search/publication?articleId=dedup_wf_001::5616457cd6557bbe26d978211488ac58
Author jsonws_user
Last Updated 22 December 2020, 19:39 (CET)
Created 22 December 2020, 19:39 (CET)