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Doctoral thesis
Open access
English

Robustness in sample selection models

ContributorsZhelonkin, Mikhail
Defense date2013-05-17
Abstract

The problem of non-random sample selectivity often occurs in practice in many different fields. In presence of sample selection, the data appears in the sample according to some selection rule. In these cases, the standard tools designed for complete samples, e.g. ordinary least squares, produce biased results, and hence, methods correcting this bias are needed. In his seminal work, Heckman proposed two estimators to solve this problem. These estimators became the backbone of the standard statistical analysis of sample selection models. However, these estimators are based on the assumption of normality and are very sensitive to small deviations from the distributional assumptions which are often not satisfied in practice. In this thesis we develop a general framework to study the robustness properties of estimators and tests in sample selection models. We use an infinitesimal approach, which allows us to explore the robustness issues and to construct robust estimators and tests.

eng
Keywords
  • Change-of-variance function
  • Heckman model
  • Influence function
  • M-estimator
  • R
  • Robust estimator
  • Robust inference
  • Sample selection
  • Time series
  • Two-stage estimator
  • Two-stage least squares
Citation (ISO format)
ZHELONKIN, Mikhail. Robustness in sample selection models. 2013. doi: 10.13097/archive-ouverte/unige:27996
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Technical informations

Creation05/14/2013 1:08:00 PM
First validation05/14/2013 1:08:00 PM
Update time03/14/2023 8:11:42 PM
Status update03/14/2023 8:11:42 PM
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