arXiv 28 Dec 2017 · Statistics — Machine Learning
arXiv:1712.10024 · PDF · Extracted main text
This paper provides estimation and inference methods for an identified set's boundary (i.e., support function) where the selection among a very large number of covariates is based on modern regularized tools. I characterize the boundary using a semiparametric moment equation. Combining Neyman-orthogonality and sample splitting ideas, I construct a root-N consistent, uniformly asymptotically Gaussian estimator of the boundary and propose a multiplier bootstrap procedure to conduct inference. I apply this result to the partially linear model, the partially linear IV model and the average partial derivative with an interval-valued outcome.
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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 | Bontemps, C., Magnac, T., and Maurin, E (2012) Set identified linear models | 0.950 | 7 | 3 | 86% |
| 2 | Beresteanu, A. and Molinari, F (2008) Asymptotic properties for a class of partially identified models | 0.941 | 6 | 3 | 83% |
| 3 | Kaido, H (2017) Asymptotically efficient estimation of weighted average derivatives with an interval censored variable | 0.920 | 9 | 5 | 78% |
| 4 | Chandrasekhar, A., Chernozhukov, V., Molinari, F., and Schrimpf, P (2012) Inference for best linear approximations to set identified functions | 0.916 | 13 | 6 | 77% |
| 5 | Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C… (2018) Double/debiased machine learning for treatment and structural parameters | 0.874 | 6 | 4 | 67% |
| 6 | Robinson, P. M (1988) Root-n-consistent semiparametric regression | 0.811 | 4 | 2 | 100% |
| 7 | Belloni, A., Chernozhukov, V., Fernandez-Val, I., and Hansen, C (2017) Program evaluation and causal inference with high-dimensional data | 0.754 | 7 | 4 | 43% |
| 8 | Gafarov, B (2019) Inference in high-dimensional set-identified affine models | 0.737 | 3 | 2 | 100% |
| 9 | Powell, J. L (1984) Least absolute deviations estimation for the censored regression model | 0.737 | 3 | 2 | 100% |
| 10 | Hardle, W. and Stoker, T (1989) Investigating smooth multiple regression by the method of average derivatives | 0.644 | 2 | 2 | 100% |
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