arXiv 19 Jun 2018 · Econometrics · 4 citations (OpenAlex)
arXiv:1806.07314 · PDF · DOI · OpenAlex · Extracted main text
It is common practice in empirical work to employ cluster-robust standard errors when using the linear regression model to estimate some structural/causal effect of interest. Researchers also often include a large set of regressors in their model specification in order to control for observed and unobserved confounders. In this paper we develop inference methods for linear regression models with many controls and clustering. We show that inference based on the usual cluster-robust standard errors by Liang and Zeger (1986) is invalid in general when the number of controls is a non-vanishing fraction of the sample size. We then propose a new clustered standard errors formula that is robust to the inclusion of many controls and allows to carry out valid inference in a variety of high-dimensional linear regression models, including fixed effects panel data models and the semiparametric partially linear model. Monte Carlo evidence supports our theoretical results and shows that our proposed variance estimator performs well in finite samples. The proposed method is also illustrated with an empirical application that re-visits 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 | Donohue III, J. J and S. D. Levitt (2001) The Impact of Legalized Abortion on Crime | 1.000 | 16 | 3 | 100% |
| 2 | Liang, K. and S. L. Zeger (2004) Longitudinal Data Analysis for Generalized Linear Models | 1.000 | 7 | 6 | 100% |
| 3 | Belloni, A., V. Chernozhukov and C. Hansen (2014) Inference on Treatment Effects after Selection among High-Dimensional Controls | 1.000 | 6 | 4 | 100% |
| 4 | Stock, J. H. and M. W. Watson (2008) Heteroskedasticity-Robust Standard Errors for Fixed Effects Panel Data Regression | 1.000 | 6 | 4 | 100% |
| 5 | Cattaneo, M. D., M. Jansson, and W. K. Newey (2018) Inference in Linear Regression Models with Many Covariates and Heteroscedasticity | 0.888 | 20 | 7 | 70% |
| 6 | Bell, R. M. and D. F. McCaffrey (2002) Bias Reduction in Standard Errors for Linear Regression with Multi-Stage Samples | 0.874 | 5 | 2 | 100% |
| 7 | Verdier, V (2018) Estimation and Inference for Linear Models with Two-Way Fixed Effects and Sparsely Matched Data | 0.843 | 3 | 3 | 100% |
| 8 | Arellano, M (1987) Computing Robust Standard Errors for Within-Group Estimators | 0.644 | 2 | 2 | 100% |
| 9 | Cattaneo, M. D., M. Jansson, and W. K. Newey (2018) Alternative Asymptotics and the Partially Linear Model with Many Regressors | 0.644 | 2 | 2 | 100% |
| 10 | Hansen, C (2007) Asymptotic Properties of a Robust Variance Matrix Estimator for Panel Data when $T$ Is Large | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 61 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | The Fragility of Sparsity | 0.644 | 2 | 2 |
| 2 | Omitted variable bias of Lasso-based inference methods: A finite sample analysis | 0.405 | 1 | 1 |
| 3 | Robust Inference in High Dimensional Linear Model with Cluster Dependence | 0.405 | 1 | 1 |