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A New Instrumental Method for Dealing with Endogenous Selection

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  • Xavier d'Haultfoeuille

    (Crest)

Abstract

This paper develops a new method for dealing with endogenous selection. When selectionis directly driven by the dependent variable, the usual instrumental strategy based onthe independence between the outcome and the instruments is likely to fail. Instead, thearticle suggests to rely on independence between the instruments and the selection variable,conditional on the outcome. This may be particularly suitable for nonignorable nonresponse,binary models with missing covariates or Roy models with unobserved sector. Nonparametricidentification of the joint distribution of variables is obtained under completeness, arank condition which has been used recently in several nonparametric instrumental problems.Even if the conditional independence between the instrument and the selection fails,the approach provides sharp bounds on parameters of interest under weaker monotonicityconditions. Apart from identification, nonparametric and parametric estimation is also considered.Eventually, the method is applied to estimate the effect of grade retention in Frenchprimary schools.

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  • Xavier d'Haultfoeuille, 2008. "A New Instrumental Method for Dealing with Endogenous Selection," Working Papers 2008-23, Center for Research in Economics and Statistics.
  • Handle: RePEc:crs:wpaper:2008-23
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    3. Joeri Smits & Jeffrey S. Racine, 2013. "Testing Exclusion Restrictions in Nonseparable Triangular Models," Department of Economics Working Papers 2013-02, McMaster University.
    4. Bian, Yuan & Yi, Grace Y. & He, Wenqing, 2024. "A unified framework of analyzing missing data and variable selection using regularized likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 194(C).
    5. D'Haultfoeuille, Xavier & Maurel, Arnaud, 2009. "Inference on a Generalized Roy Model, with an Application to Schooling Decisions in France," IZA Discussion Papers 4606, Institute of Labor Economics (IZA).
    6. Li, Wei & Luo, Shanshan & Xu, Wangli, 2024. "Calibrated regression estimation using empirical likelihood under data fusion," Computational Statistics & Data Analysis, Elsevier, vol. 190(C).
    7. Shengfang Tang & Zongwu Cai & Ying Fang & Ming Lin, 2019. "Testing Unconfoundedness Assumption Using Auxiliary Variables," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201905, University of Kansas, Department of Economics, revised Mar 2019.

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