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A two-stage Bridge estimator for regression models with endogeneity based on control function method

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Abstract

In this study, we investigate a penalty-based two-stage least square estimator in regression models when the exploratory variables are correlated with the error term. We propose a two-stage Bridge estimator to overcome this endogeneity problem in high-dimensional data. Our proposed estimator enjoys remarkable statistical properties such as consistency and asymptotic normality. As special cases, this method deals some ill-condition situations such as the multicollinearity as well as the sparsity. Performance of the proposed estimators is demonstrated by simulation studies and it is compared to the existing estimators. An application in real data set is presented for illustration.

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Notes

  1. We use the R software and all codes are available upon request.

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Authors and Affiliations

  1. Department of Statistics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran

    Fatemeh Bahador, Ayyub Sheikhi & Alireza Arabpour

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  1. Fatemeh Bahador
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  2. Ayyub Sheikhi
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  3. Alireza Arabpour
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Correspondence to Ayyub Sheikhi.

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Bahador, F., Sheikhi, A. & Arabpour, A. A two-stage Bridge estimator for regression models with endogeneity based on control function method. Comput Stat 39, 1351–1370 (2024). https://doi.org/10.1007/s00180-023-01379-9

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