arXiv 26 Jun 2020 · Econometrics · 2 citations (OpenAlex)
arXiv:2006.14998 · PDF · DOI · OpenAlex · Extracted main text
Instrumental variables (IV) regression is a popular method for the estimation of the endogenous treatment effects. Conventional IV methods require all the instruments are relevant and valid. However, this is impractical especially in high-dimensional models when we consider a large set of candidate IVs. In this paper, we propose an IV estimator robust to the existence of both the invalid and irrelevant instruments (called R2IVE) for the estimation of endogenous treatment effects. This paper extends the scope of Kang et al. (2016) by considering a true high-dimensional IV model and a nonparametric reduced form equation. It is shown that our procedure can select the relevant and valid instruments consistently and the proposed R2IVE is root-n consistent and asymptotically normal. Monte Carlo simulations demonstrate that the R2IVE performs favorably compared to the existing high-dimensional IV estimators (such as, NAIVE (Fan and Zhong, 2018) and sisVIVE (Kang et al., 2016)) when invalid instruments exist. In the empirical study, we revisit the classic question of trade and growth (Frankel and Romer, 1999).
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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 | Fan, Q., Zhong, W (2018) Nonparametric additive instrumental variable estimator: A group shrinkage estimation perspective self | 1.000 | 12 | 5 | 100% |
| 2 | Kang, H., Zhang, A., Cai, T. T., Small, D. S (2016) Instrumental variables estimation with some invalid instruments and its application to mendelian randomization | 1.000 | 11 | 6 | 100% |
| 3 | Frankel, J., Romer, D (1999) Does trade causes growth? American Economic Review 89, 379–399 | 0.843 | 3 | 3 | 100% |
| 4 | Zou, H., Zhang, H (2009) On the adaptive elastic-net with a diverging number of parameters | 0.737 | 5 | 2 | 60% |
| 5 | Huang, J., Horowitz, J., Wei, F (2010) Variable selection in nonparametric additive models | 0.737 | 4 | 3 | 50% |
| 6 | Belloni, A., Chen, D., Chernozhukov, V., Hansen, C (2012) Sparse models and methods for optimal instruments with an application to eminent domain | 0.644 | 2 | 2 | 100% |
| 7 | Chen, J., Chen, Z (2008) Extended bayesian information criteria for model selection with large model spaces | 0.644 | 2 | 2 | 100% |
| 8 | Wang, H., Li, R., Tsai, C.-L (2007) Tuning parameter selectors for the smoothly clipped absolute deviation method | 0.644 | 2 | 2 | 100% |
| 9 | Zou, H., Hastie, T (2005) Regularization and variable selection via the elastic nets | 0.511 | 2 | 1 | 100% |
| 10 | Newey, W (1990) Efficient instrumental variable estimation on nonlinear models | 0.511 | 2 | 1 | 100% |
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