Jooyoung Cha, Harold D. Chiang, Yuya Sasaki
arXiv 21 Aug 2021 · Econometrics · publishedThe Review of Economics and Statistics (2023) · 1 citations (OpenAlex)
arXiv:2108.09520 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes a new method of inference in high-dimensional regression models and high-dimensional IV regression models. Estimation is based on a combined use of the orthogonal greedy algorithm, high-dimensional Akaike information criterion, and double/debiased machine learning. The method of inference for any low-dimensional subvector of high-dimensional parameters is based on a root-$N$ asymptotic normality, which is shown to hold without requiring the exact sparsity condition or the $L^p$ sparsity condition. Simulation studies demonstrate superior finite-sample performance of this proposed method over those based on the LASSO or the random forest, especially under less sparse models. We illustrate an application to production analysis with a panel of Chilean firms.
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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 | Belloni, A., V. Chernozhukov, and C. Hansen (2013) Inference on Treatment Effects after Selection among High-Dimensional Controls† | 0.956 | 8 | 4 | 88% |
| 2 | Levinsohn, J. and A. Petrin (2003) Estimating production functions using inputs to control for unobservables | 0.874 | 11 | 2 | 100% |
| 3 | Olley, G. S. and A. Pakes (1996) The Dynamics of Productivity in the Telecommunications Equipment Industry | 0.644 | 4 | 1 | 100% |
| 4 | Robinson, P (1988) Root- N-Consistent Semiparametric Regression | 0.644 | 2 | 2 | 100% |
| 5 | Belloni, A., D. Chen, V. Chernozhukov, and C. Hansen (2012) Sparse models and methods for optimal instruments with an application to eminent domain | 0.644 | 2 | 2 | 100% |
| 6 | Ing, C.-K (2020) Model selection for high-dimensional linear regression with dependent observations | 0.630 | 36 | 8 | 25% |
| 7 | Chernozhukov, V., D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W… (2018) Double/debiased machine learning for treatment and structural parameters | 0.585 | 20 | 6 | 20% |
| 8 | Ackerberg, D. A., K. Caves, and G. Frazer (2015) Identification properties of recent production function estimators | 0.511 | 2 | 1 | 100% |
| 9 | Petrin, A., B. P. Poi, and J. Levinsohn (2004) Production function estimation in Stata using inputs to control for unobservables | 0.511 | 2 | 1 | 100% |
| 10 | Temlyakov, V. N (2000) Weak greedy algorithms | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 45 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Local Projections Inference with High-dimensional Covariates without Sparsity | 0.405 | 1 | 1 |
| 2 | Treatment Effects Inference with High-Dimensional Instruments and Control Variables | 0.405 | 1 | 1 |