Robust Analysis of Sample Selection Models through the R Package ssmrob
Dublin Core
Title
Robust Analysis of Sample Selection Models through the R Package ssmrob
Subject
endogenous treatment model, R, robust estimation, robust inference, sample selection models, two-step estimator
Description
The aim of this paper is to describe the implementation and to provide a tutorial for
the R package ssmrob, which is developed for robust estimation and inference in sample
selection and endogenous treatment models. The sample selectivity issue occurs in practice in various fields, when a non-random sample of a population is observed, i.e., when
observations are present according to some selection rule. It is well known that the classical estimators introduced by Heckman (1979) are very sensitive to small deviations from
the distributional assumptions (typically the normality assumption on the error terms).
Zhelonkin, Genton, and Ronchetti (2016) investigated the robustness properties of these
estimators and proposed robust alternatives to the estimator and the corresponding test.
We briefly discuss the robust approach and demonstrate its performance in practice by
providing several empirical examples. The package can be used both to produce a complete robust statistical analysis of these models which complements the classical one and
as a set of useful tools for exploratory data analysis. Specifically, robust estimators and
standard errors of the coefficients of both the selection and the regression equations are
provided together with a robust test of selectivity. The package therefore provides additional useful information to practitioners in different fields of applications by enhancing
their statistical analysis of these models.
the R package ssmrob, which is developed for robust estimation and inference in sample
selection and endogenous treatment models. The sample selectivity issue occurs in practice in various fields, when a non-random sample of a population is observed, i.e., when
observations are present according to some selection rule. It is well known that the classical estimators introduced by Heckman (1979) are very sensitive to small deviations from
the distributional assumptions (typically the normality assumption on the error terms).
Zhelonkin, Genton, and Ronchetti (2016) investigated the robustness properties of these
estimators and proposed robust alternatives to the estimator and the corresponding test.
We briefly discuss the robust approach and demonstrate its performance in practice by
providing several empirical examples. The package can be used both to produce a complete robust statistical analysis of these models which complements the classical one and
as a set of useful tools for exploratory data analysis. Specifically, robust estimators and
standard errors of the coefficients of both the selection and the regression equations are
provided together with a robust test of selectivity. The package therefore provides additional useful information to practitioners in different fields of applications by enhancing
their statistical analysis of these models.
Creator
Mikhail Zhelonkin
Source
https://www.jstatsoft.org/article/view/v099i04
Publisher
Erasmus University Rotterdam
Date
August 2021
Contributor
Fajar bagus W
Format
PDF
Language
Inggris
Type
Text
Files
Collection
Citation
Mikhail Zhelonkin, “Robust Analysis of Sample Selection Models through the R Package ssmrob,” Repository Horizon University Indonesia, accessed May 7, 2025, https://repository.horizon.ac.id/items/show/8205.