arXiv 11 Dec 2022 · Econometrics
arXiv:2212.05554 · PDF · DOI · OpenAlex · Extracted main text
Cluster standard error (Liang and Zeger, 1986) is widely used by empirical researchers to account for cluster dependence in linear model. It is well known that this standard error is biased. We show that the bias does not vanish under high dimensional asymptotics by revisiting Chesher and Jewitt (1987)'s approach. An alternative leave-cluster-out crossfit (LCOC) estimator that is unbiased, consistent and robust to cluster dependence is provided under high dimensional setting introduced by Cattaneo, Jansson and Newey (2018). Since LCOC estimator nests the leave-one-out crossfit estimator of Kline, Saggio and Solvsten (2019), the two papers are unified. Monte Carlo comparisons are provided to give insights on its finite sample properties. The LCOC estimator is then applied to Angrist and Lavy's (2009) study of the effects of high school achievement award and Donohue III and Levitt's (2001) study of the impact of abortion on crime.
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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 | LIANG, K.-Y. and ZEGER, S. L (1986) Longitudinal data analysis using generalized linear models | 0.405 | 1 | 1 | 100% |
| 2 | Young, A (2018) Channeling Fisher: Randomization Tests and the Statistical Insignificance of Seemingly Significant Experimental Results* | 0.405 | 1 | 1 | 100% |
| 3 | Donohue, John J., I. and Levitt, S. D (2001) The Impact of Legalized Abortion on Crime* | 0.405 | 1 | 1 | 100% |
| 4 | Imbens, G. W. and Kolesár, M (2016) Robust Standard Errors in Small Samples: Some Practical Advice | 0.405 | 1 | 1 | 100% |
| 5 | Verdier, V (2020) Estimation and Inference for Linear Models with Two-Way Fixed Effects and Sparsely Matched Data | 0.405 | 1 | 1 | 100% |
| 6 | Angrist, J. and Lavy, V (2009) The effects of high stakes high school achievement awards: Evidence from a randomized trial | 0.405 | 1 | 1 | 100% |
| 7 | Belloni, A., Chernozhukov, V., and Hansen, C (2014) High-dimensional methods and inference on structural and treatment effects | 0.405 | 1 | 1 | 100% |
| 8 | Chesher, A. and Jewitt, I (1987) The bias of a heteroskedasticity consistent covariance matrix estimator | 0.405 | 1 | 1 | 100% |
| 9 | Anatolyev, S (2018) Almost unbiased variance estimation in linear regressions with many covariates | 0.405 | 1 | 1 | 100% |
| 10 | Bell, R. and Mccaffrey, D (2002) Bias reduction in standard errors for linear regression with multi-stage samples | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 21 scored citations.