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SemiParSampleSel (version 1.2)

SemiParSampleSel-package: Semiparametric Sample Selection Modelling with Continuous or Discrete Response

Description

SemiParSampleSel provides a function for fitting continuous and discrete response (copula) sample selection models with parametric and nonparametric predictor effects. Several bivariate copula distributions are supported. Smoothness selection is achieved automatically and interval calculations are based on a Bayesian approach.

Arguments

Details

SemiParSampleSel provides a function for flexible sample selection modelling with continuous or discrete response. The underlying representation and estimation of the model is based on a penalized regression spline approach, with automatic smoothness selection. The numerical routine carries out function minimization using a trust region algorithm from the package trust in combination with an adaptation of a low level smoothness selection fitting procedure from the package mgcv. SemiParSampleSel supports the use of many smoothers as extracted from mgcv. Scale invariant tensor product smooths are not currently supported. Estimation is by penalized maximum likelihood with automatic smoothness selection by approximate Un-Biased Risk Estimator (UBRE) score, which can also be viewed as an approximate AIC. The depedence between the selection and outcome equations is modelled through the use of copulas. Confidence intervals for smooth components and nonlinear functions of the model parameters are derived using a Bayesian approach. Approximate p-values for testing individual smooth terms for equality to the zero function are also provided. Functions plot.SemiParSampleSel and summary.SemiParSampleSel extract such information from a fitted SemiParSampleSel object. Model/variable selection is also possible via the use of shrinakge smoothers or information criteria. Function est.aver calculates the average outcome corrected for non-random sample selection.

References

Marra G. and Radice R. (2013), Estimation of a Regression Spline Sample Selection Model. Computational Statistics and Data Analysis, 61, 158-173. Wojtys M., Marra G. and Radice R. (submitted), Copula Regression Spline Sample Selection Models: The R Package SemiParSampleSel.

See Also

SemiParSampleSel