Harold D Chiang, Yukun Ma, Joel Rodrigue, Yuya Sasaki
arXiv 8 Oct 2021 · Econometrics · 2 citations (OpenAlex)
arXiv:2110.04365 · PDF · DOI · OpenAlex · Extracted main text
This paper presents novel methods and theories for estimation and inference about parameters in econometric models using machine learning for nuisance parameters estimation when data are dyadic. We propose a dyadic cross fitting method to remove over-fitting biases under arbitrary dyadic dependence. Together with the use of Neyman orthogonal scores, this novel cross fitting method enables root-$n$ consistent estimation and inference robustly against dyadic dependence. We illustrate an application of our general framework to high-dimensional network link formation models. With this method applied to empirical data of international economic networks, we reexamine determinants of free trade agreements (FTA) viewed as links formed in the dyad composed of world economies. We document that standard methods may lead to misleading conclusions for numerous classic determinants of FTA formation due to biased point estimates or standard errors which are too small.
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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 | Kallenberg, O (2006) Probabilistic symmetries and invariance principles | 0.928 | 5 | 3 | 80% |
| 2 | Chiang, H. D., K. Kato, Y. Ma, and Y. Sasaki (2021) a): Multiway Cluster Robust Double/Debiased Machine Learning self | 0.920 | 9 | 5 | 78% |
| 3 | Baier, S. L. and J. H. Bergstrand (2004) Economic determinants of free trade agreements | 0.874 | 19 | 2 | 100% |
| 4 | Chernozhukov, V., D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W… (2018) a): Double/debiased machine learning for treatment and structural parameters | 0.849 | 18 | 6 | 61% |
| 5 | Chiang, H. D., K. Kato, and Y. Sasaki (2021) b): Inference for high-dimensional exchangeable arrays self | 0.794 | 6 | 4 | 50% |
| 6 | Belloni, A., V. Chernozhukov, D. Chetverikov, and Y. Wei (2018) Uniformly valid post-regularization confidence regions for many functional parameters in z-estimation framework | 0.773 | 13 | 4 | 46% |
| 7 | Belloni, A., V. Chernozhukov, and Y. Wei (2016) Post-selection inference for generalized linear models with many controls | 0.763 | 9 | 3 | 44% |
| 8 | Frankel, J. A., E. Stein, and S.-J. Wei (1993) Continental trading blocs: are they natural, or super-natural? Tech | 0.737 | 3 | 2 | 100% |
| 9 | Frankel, J., E. Stein, and S.-J. Wei (1995) Trading blocs and the Americas: The natural, the unnatural, and the super-natural | 0.737 | 3 | 2 | 100% |
| 10 | Frankel, J. A., E. Stein, and S.-J. Wei (1996) Regional trading arrangement: natural or super-natural? Tech | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 88 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Debiased Machine Learning U-Statistics | 0.405 | 1 | 1 |