Edvard Bakhitov, Amandeep Singh
arXiv 15 Jan 2021 · Econometrics · publishedProceedings of the 23rd ACM Conference on Economics and Computation (2022) · 14 citations (OpenAlex)
arXiv:2101.06078 · PDF · DOI · OpenAlex · Extracted main text
Recent advances in the literature have demonstrated that standard supervised learning algorithms are ill-suited for problems with endogenous explanatory variables. To correct for the endogeneity bias, many variants of nonparameteric instrumental variable regression methods have been developed. In this paper, we propose an alternative algorithm called boostIV that builds on the traditional gradient boosting algorithm and corrects for the endogeneity bias. The algorithm is very intuitive and resembles an iterative version of the standard 2SLS estimator. Moreover, our approach is data driven, meaning that the researcher does not have to make a stance on neither the form of the target function approximation nor the choice of instruments. We demonstrate that our estimator is consistent under mild conditions. We carry out extensive Monte Carlo simulations to demonstrate the finite sample performance of our algorithm compared to other recently developed methods. We show that boostIV is at worst on par with the existing methods and on average significantly outperforms them.
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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 | Friedman, Jerome H (2001) Greedy function approximation: a gradient boosting machine | 1.000 | 5 | 3 | 100% |
| 2 | Zhang, Tong, Yu, Bin (2005) Boosting with early stopping: Convergence and consistency | 0.838 | 17 | 4 | 59% |
| 3 | Newey, Whitney K, Powell, James L (2003) Instrumental variable estimation of nonparametric models | 0.811 | 4 | 2 | 100% |
| 4 | Bühlmann, Peter, Hothorn, Torsten (2007) Boosting algorithms: Regularization, prediction and model fitting | 0.737 | 3 | 2 | 100% |
| 5 | Chamberlain, Gary (1987) Asymptotic efficiency in estimation with conditional moment restrictions | 0.737 | 3 | 2 | 100% |
| 6 | Hartford, Jason, Lewis, Greg, Leyton-Brown, Kevin, Taddy, Matt (2017) Deep IV: A flexible approach for counterfactual prediction | 0.737 | 3 | 2 | 100% |
| 7 | Berry, Steven T, Haile, Philip A (2014) Identification in differentiated products markets using market level data | 0.693 | 5 | 1 | 100% |
| 8 | Breiman, Leo (1998) Arcing classifiers | 0.644 | 2 | 2 | 100% |
| 9 | Bühlmann, Peter, Yu, Bin (2003) Boosting with the L 2 loss: regression and classification | 0.644 | 2 | 2 | 100% |
| 10 | Schapire, Robert E (1990) The strength of weak learnability | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 34 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Penalized GMM Framework for Inference on Functionals of Nonparametric Instrumental Variable Estimators | 0.644 | 2 | 2 |
| 2 | Causal Bandits: Online Decision-Making in Endogenous Settings | 0.405 | 1 | 1 |