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Copula Regression Spline Sample Selection Models: The R Package SemiParSampleSel

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  • Wojtyś, Magorzata
  • Marra, Giampiero
  • Radice, Rosalba

Abstract

Sample selection models deal with the situation in which an outcome of interest is observed for a restricted non-randomly selected sample of the population. The estimation of these models is based on a binary equation, which describes the selection process, and an outcome equation, which is used to examine the substantive question of interest. Classic sample selection models assume a priori that continuous covariates have a linear or pre-specified non-linear relationship to the outcome, and that the distribution linking the two equations is bivariate normal. We introduce the R package SemiParSampleSel which implements copula regression spline sample selection models. The proposed implementation can deal with non-random sample selection, non-linear covariate-response relationships, and non-normal bivariate distributions between the model equations. We provide details of the model and algorithm and describe the implementation in SemiParSampleSel. The package is illustrated using simulated and real data examples.

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  • Wojtyś, Magorzata & Marra, Giampiero & Radice, Rosalba, 2016. "Copula Regression Spline Sample Selection Models: The R Package SemiParSampleSel," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 71(i06).
    • Handle: RePEc:jss:jstsof:v:071:i06
    DOI: http://hdl.handle.net/10.18637/jss.v071.i06
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    5. David Zimmer, 2018. "Using copulas to estimate the coefficient of a binary endogenous regressor in a Poisson regression: Application to the effect of insurance on doctor visits," Health Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 545-556, March.
    6. Maciej Berȩsewicz & Dagmara Nikulin, 2021. "Estimation of the size of informal employment based on administrative records with non‐ignorable selection mechanism," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(3), pages 667-690, June.
    7. Tibi Didier Zoungrana, 2021. "The effect of wealth on the choice of household drinking water sources in West Africa," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2241-2250, April.
    8. Marra, Giampiero & Wyszynski, Karol, 2016. "Semi-parametric copula sample selection models for count responses," Computational Statistics & Data Analysis, Elsevier, vol. 104(C), pages 110-129.
    9. Karol Wyszynski & Giampiero Marra, 2018. "Sample selection models for count data in R," Computational Statistics, Springer, vol. 33(3), pages 1385-1412, September.
    10. David M. Zimmer, 2022. "Investigating the dynamic interdependency between poverty and marital separation," Review of Economics of the Household, Springer, vol. 20(4), pages 1239-1254, December.
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    13. Maciej Berk{e}sewicz & Dagmara Nikulin, 2019. "Estimation of the size of informal employment based on administrative records with non-ignorable selection mechanism," Papers 1906.10957, arXiv.org.
    14. Nicolai Hans & Nadja Klein & Florian Faschingbauer & Michael Schneider & Andreas Mayr, 2023. "Boosting distributional copula regression," Biometrics, The International Biometric Society, vol. 79(3), pages 2298-2310, September.
    15. Wojtyś, Małgorzata & Marra, Giampiero & Radice, Rosalba, 2018. "Copula based generalized additive models for location, scale and shape with non-random sample selection," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 1-14.
    16. Giampiero Marra & Rosalba Radice & David M. Zimmer, 2020. "Estimating the binary endogenous effect of insurance on doctor visits by copula‐based regression additive models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 953-971, August.
    17. Schmidt, Rouven & Kneib, Thomas, 2023. "Multivariate distributional stochastic frontier models," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    18. Hamori, Shigeyuki & Motegi, Kaiji & Zhang, Zheng, 2019. "Calibration estimation of semiparametric copula models with data missing at random," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 85-109.
    19. Machado, Robson J.M. & van den Hout, Ardo & Marra, Giampiero, 2021. "Penalised maximum likelihood estimation in multi-state models for interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).
    20. Pierfrancesco Alaimo Di Loro & Daria Scacciatelli & Giovanna Tagliaferri, 2023. "2-step Gradient Boosting approach to selectivity bias correction in tax audit: an application to the VAT gap in Italy," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(1), pages 237-270, March.
    21. Chiara Mussida & Luca Zanin, 2020. "I found a better job opportunity! Voluntary job mobility of employees and temporary contracts before and after the great recession in France, Italy and Spain," Empirical Economics, Springer, vol. 59(1), pages 47-98, July.
    22. Geminiani, Elena & Marra, Giampiero & Moustaki, Irini, 2021. "Single and multiple-group penalized factor analysis: a trust-region algorithm approach with integrated automatic multiple tuning parameter selection," LSE Research Online Documents on Economics 108873, London School of Economics and Political Science, LSE Library.
    23. Huihui Lin & N. Rao Chaganty, 2021. "Multivariate distributions of correlated binary variables generated by pair-copulas," Journal of Statistical Distributions and Applications, Springer, vol. 8(1), pages 1-14, December.
    24. Burli, Pralhad & Lal, Pankaj & Wolde, Bernabas & Jose, Shibu & Bardhan, Sougata, 2021. "Perceptions about switchgrass and land allocation decisions: Evidence from a farmer survey in Missouri," Land Use Policy, Elsevier, vol. 109(C).

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