Matias D. Cattaneo, Michael Jansson, Xinwei Ma
arXiv 30 Sep 2020 · Econometrics · publishedJournal of Econometrics (2021) · 33 citations (OpenAlex)
arXiv:2009.14367 · PDF · DOI · OpenAlex · Extracted main text
This paper investigates the large sample properties of local regression distribution estimators, which include a class of boundary adaptive density estimators as a prime example. First, we establish a pointwise Gaussian large sample distributional approximation in a unified way, allowing for both boundary and interior evaluation points simultaneously. Using this result, we study the asymptotic efficiency of the estimators, and show that a carefully crafted minimum distance implementation based on "redundant" regressors can lead to efficiency gains. Second, we establish uniform linearizations and strong approximations for the estimators, and employ these results to construct valid confidence bands. Third, we develop extensions to weighted distributions with estimated weights and to local $L^{2}$ least squares estimation. Finally, we illustrate our methods with two applications in program evaluation: counterfactual density testing, and IV specification and heterogeneity density analysis. Companion software packages in Stata and R are available.
appendix boundary found by none_found · 100% of the source is main text. Read the extracted text to check this.
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 | Cattaneo, Jansson, and Ma (2020) Simple Local Polynomial Density Estimators self | 0.928 | 4 | 3 | 100% |
| 2 | Granovsky and Müller (1991) Optimizing Kernel Methods: A Unifying Variational Principle | 0.585 | 3 | 1 | 100% |
| 3 | Giné, Koltchinskii, and Sakhanenko (2004) Kernel Density Estimators: Convergence in Distribution for Weighted Sup-Norms | 0.511 | 2 | 1 | 100% |
| 4 | de la Peña and Montgomery-Smith (1995) Decoupling Inequalities for the Tail Probabilities of Multivariate U-statistics | 0.405 | 1 | 1 | 100% |
| 5 | Giné, Lataa, and Zinn (2000) Exponential and Moment Inequalities for U-statistics | 0.405 | 1 | 1 | 100% |
| 6 | Loader (2006) Local Regression and Likelihood | 0.405 | 1 | 1 | 100% |
| 7 | Chernozhukov, Chetverikov, Kato, and Koike (2019) Improved Central Limit Theorem and Bootstrap Approximations in High Dimensions | 0.405 | 1 | 1 | 100% |
Showing the top 7 of 7 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.