R package SemiParBIVProbit: Semiparametric Bivariate Probit Modelling. Routine for fitting bivariate probit models with semiparametric predictors (including linear and nonlinear effects) in the presence of correlated error equations, endogeneity or sample selection. Bivariate copula models are also supported.
Keywords for this software
References in zbMATH (referenced in 10 articles )
Showing results 1 to 10 of 10.
- Han, Sukjin; Vytlacil, Edward J.: Identification in a generalization of bivariate probit models with dummy endogenous regressors (2017)
- Marra, Giampiero; Radice, Rosalba: A joint regression modeling framework for analyzing bivariate binary data in $\mathsfR$ (2017)
- Klein, Nadja; Kneib, Thomas: Simultaneous inference in structured additive conditional copula regression models: a unifying Bayesian approach (2016)
- M. Wojtys; Giampiero Marra; Rosalba Radice: Copula Regression Spline Sample Selection Models: The R Package SemiParSampleSel (2016)
- Radice, Rosalba; Marra, Giampiero; Wojtyś, Małgorzata: Copula regression spline models for binary outcomes (2016)
- Marra, Giampiero; Radice, Rosalba; Missiroli, Silvia: Testing the hypothesis of absence of unobserved confounding in semiparametric bivariate probit models (2014)
- Marra, Giampiero: On $p$-values for semiparametric bivariate probit models (2013)
- Marra, Giampiero; Papageorgiou, Georgios; Radice, Rosalba: Estimation of a semiparametric recursive bivariate probit model with nonparametric mixing (2013)
- Marra, Giampiero; Radice, Rosalba: A penalized likelihood estimation approach to semiparametric sample selection binary response modeling (2013)
- Marra, Giampiero; Radice, Rosalba: Estimation of a semiparametric recursive bivariate probit model in the presence of endogeneity (2011)