Jannis Kueck, Ye Luo, Martin Spindler, Zigan Wang
arXiv 31 Dec 2017 · Statistics — Machine Learning · publishedJournal of Econometrics (2022) · 12 citations (OpenAlex)
arXiv:1801.00364 · PDF · DOI · OpenAlex · Extracted main text
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 $L_2$-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-$L_2$-Boosting and orthogonal $L_2$-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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Peter Bühlmann \ Torsten Hothorn (2007) Boosting Algorithms: Regularization, Prediction and Model Fitting | 0.874 | 5 | 2 | 100% |
| 2 | Ross Levine, Chen Lin \ Zigan Wang (2020) Bank Networks and Acquisitions | 0.811 | 15 | 2 | 80% |
| 3 | Alexandre Belloni, Victor Chernozhukov \ Christian Hansen (2014) Inference on Treatment Effects After Selection Amongst High-Dimensional Controls | 0.754 | 7 | 4 | 43% |
| 4 | A. Belloni, D. Chen, V. Chernozhukov \ C. Hansen (2012) Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain | 0.737 | 5 | 3 | 40% |
| 5 | V. Chernozhukov, D. Chetverikov, M. Demirer, E. Duflo, C. Hansen \ a… (2016) Double Machine Learning for Treatment and Causal Parameters | 0.737 | 3 | 2 | 100% |
| 6 | Ye Luo \ Martin Spindler (2016) High-Dimensional L2Boosting: Rate of Convergence | 0.737 | 3 | 2 | 100% |
| 7 | Alexandre Belloni \ Victor Chernozhukov (2013) Least squares after model selection in high-dimensional sparse models | 0.644 | 2 | 2 | 100% |
| 8 | Peter Bühlmann, Bin Yu, Yoram Singer \ Larry Wasserman (2006) Sparse Boosting | 0.644 | 2 | 2 | 100% |
| 9 | Peter Bühlmann \ Bin Yu (2003) Boosting with the $l_2$ Loss: Regression and Classification | 0.644 | 2 | 2 | 100% |
| 10 | Bradley Efron, Trevor Hastie, Iain Johnstone, Robert Tibshirani et al (2004) Least angle regression | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 32 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Inference in High-Dimensional Regression Models without the Exact or $L^p$ sparsity | 0.405 | 1 | 1 |
| 2 | The boosted HP filter is more general than you might think | 0.405 | 1 | 1 |