Conditional independence in sample selection models
AbstractIn this note, I describe the conditional independence properties that make it possible to use the selection propensity score to control selection bias in a general sample selection model. The resulting characterization does not rely on a latent index selection mechanism and imposes no structure other than an assumption of independance between the regression error term and the regressors in random samples. This approach leads to a simple rule that can be used to determine if conditioning on the selection propensity score is sufficient to control selection bias.
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Bibliographic InfoArticle provided by Elsevier in its journal Economics Letters.
Volume (Year): 54 (1997)
Issue (Month): 2 (February)
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Web page: http://www.elsevier.com/locate/ecolet
Other versions of this item:
- Angrist, J.D., 1996. "Conditional Independance in Sample Selection Models," Working papers 96-27, Massachusetts Institute of Technology (MIT), Department of Economics.
- C5 - Mathematical and Quantitative Methods - - Econometric Modeling
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
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