arXiv 7 Mar 2024 · Econometrics · publishedJournal of Econometrics (2025)
arXiv:2403.04766 · PDF · DOI · OpenAlex · Extracted main text
This paper develops a general asymptotic theory for nonparametric kernel regression in the presence of cluster dependence. We examine nonparametric density estimation, Nadaraya-Watson kernel regression, and local linear estimation. Our theory accommodates growing and heterogeneous cluster sizes. We derive asymptotic conditional bias and variance, establish uniform consistency, and prove asymptotic normality. Our findings reveal that under heterogeneous cluster sizes, the asymptotic variance includes a new term reflecting within-cluster dependence, which is overlooked when cluster sizes are presumed to be bounded. We propose valid approaches for bandwidth selection and inference, introduce estimators of the asymptotic variance, and demonstrate their consistency. In simulations, we verify the effectiveness of the cluster-robust bandwidth selection and show that the derived cluster-robust confidence interval improves the coverage ratio. We illustrate the application of these methods using a policy-targeting dataset in development economics.
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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 | Hansen, B. E. and Lee, S (2019) Asymptotic theory for clustered samples | 0.928 | 5 | 4 | 80% |
| 2 | Alatas, V., Banerjee, A., Hanna, R., Olken, B. A. and Tobias, J (2012) Targeting the poor: evidence from a field experiment in indonesia | 0.874 | 10 | 2 | 100% |
| 3 | Bugni, F., Canay, I., Shaikh, A. and Tabord-Meehan, M (2022) Inference for cluster randomized experiments with non-ignorable cluster sizes | 0.811 | 4 | 2 | 100% |
| 4 | Fan, J. and Gijbels, I (1996) Local Polynomial Modelling and Its Applications: Monographs on Statistics and Applied Probability | 0.811 | 4 | 2 | 100% |
| 5 | Hansen, B. E (2022) a) | 0.811 | 4 | 2 | 100% |
| 6 | MacKinnon, J. G., Nielsen, M. . and Webb, M. D (2022) Cluster-robust inference: A guide to empirical practice | 0.737 | 3 | 2 | 100% |
| 7 | Armstrong, T. B. and Kolesár, M (2018) Optimal inference in a class of regression models | 0.644 | 2 | 2 | 100% |
| 8 | Bartalotti, O. and Brummet, Q (2017) Regression discontinuity designs with clustered data, in | 0.644 | 2 | 2 | 100% |
| 9 | Bhattacharya, D (2005) Asymptotic inference from multi-stage samples | 0.644 | 2 | 2 | 100% |
| 10 | Fan, J. and Gijbels, I (1992) Variable bandwidth and local linear regression smoothers | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 42 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.