Estimation and inference of treatment effects with L2-boosting in high-dimensional settings

Jannis Kueck, Ye Luo, Martin Spindler and Zigan Wang

Journal of Econometrics, 2023, vol. 234, issue 2, 714-731

Abstract: Empirical researchers are increasingly faced with rich data sets containing many controls or instrumental variables, making it essential to choose an appropriate approach to variable selection. In this paper, we provide results for valid inference after post- or orthogonal L2-boosting is used for variable selection. We consider treatment effects after selecting among many control variables and instrumental variable models with potentially many instruments. To achieve this, we establish new results for the rate of convergence of iterated post-L2-boosting and orthogonal L2-boosting in a high-dimensional setting similar to Lasso, i.e., under approximate sparsity without assuming the beta-min condition. These results are extended to the 2SLS framework and valid inference is provided for treatment effect analysis. We give extensive simulation results for the proposed methods and compare them with Lasso. In an empirical application, we construct efficient IVs with our proposed methods to estimate the effect of pre-merger overlap of bank branch networks in the US on the post-merger stock returns of the acquirer bank.

Keywords: L2-boosting; Treatment effects; Instrumental variables; Post-selection inference; High-dimensional data (search for similar items in EconPapers)
JEL-codes: C12 C21 C36 C55 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:234:y:2023:i:2:p:714-731

DOI: 10.1016/j.jeconom.2022.02.005

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