Sample selection models without exclusion restrictions: Parameter heterogeneity and partial identification

Arbeitspapier

This paper studies semiparametric versions of the classical sample selection model (Heckman (1976, 1979)) without exclusion restrictions. We extend the analysis in Honor'e and Hu (2020) by allowing for parameter heterogeneity and derive implications of this model. We also consider models that allow for heteroskedasticity and briefly discuss other extensions. The key ideas are illustrated in a simple wage regression for females. We find that the derived implications of a semiparametric version of Heckman's classical sample selection model are consistent with the data for women with no college education, but strongly rejected for women with a college degree or more.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Paper ; No. WP 2022-33

Classification and Topics

• Classification: Wirtschaft ; Econometrics ; Semiparametric and Nonparametric Methods: General ; Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions ; Single Equation Models; Single Variables: Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
• Subject: Selection ; Heterogeneity ; Heteroskedasticity ; Exclusion Restrictions ; Identification

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Honoré, Bo E. ; Hu, Luojia
• Event: Veröffentlichung
• (who): Federal Reserve Bank of Chicago
• (where): Chicago, IL
• (when): 2022

Further information

• DOI: doi:10.21033/wp-2022-33
• Handle: http://hdl.handle.net/10419/267988
• Last update: 10.03.2025, 11:44 AM CET