Denis Chetverikov, Jinyong Hahn, Zhipeng Liao, Andres Santos
arXiv 18 Mar 2023 · Econometrics
arXiv:2303.10306 · PDF · DOI · OpenAlex · Extracted main text
We examine asymptotic properties of the OLS estimator when the values of the regressor of interest are assigned randomly and independently of other regressors. We find that the OLS variance formula in this case is often simplified, sometimes substantially. In particular, when the regressor of interest is independent not only of other regressors but also of the error term, the textbook homoskedastic variance formula is valid even if the error term and auxiliary regressors exhibit a general dependence structure. In the context of randomized controlled trials, this conclusion holds in completely randomized experiments with constant treatment effects. When the error term is heteroscedastic with respect to the regressor of interest, the variance formula has to be adjusted not only for heteroscedasticity but also for correlation structure of the error term. However, even in the latter case, some simplifications are possible as only a part of the correlation structure of the error term should be taken into account. In the context of randomized control trials, this implies that the textbook homoscedastic variance formula is typically not valid if treatment effects are heterogenous but heteroscedasticity-robust variance formulas are valid if treatment effects are independent across units, even if the error term exhibits a general dependence structure. In addition, we extend the results to the case when the regressor of interest is assigned randomly at a group level, such as in randomized control trials with treatment assignment determined at a group (e.g., school/village) level.
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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 | Moulton (1986) Random group effects and the precision of regression estimates | 1.000 | 5 | 3 | 100% |
| 2 | Liang and Zeger (1986) Longitudinal Data Analysis Using Generalized Linear Models | 0.737 | 3 | 2 | 100% |
| 3 | Abadie, Athey, Imbens, and Wooldridge (2017) When should you adjust standard errors for clustering? | 0.585 | 3 | 1 | 100% |
| 4 | Barrios, Diamond, Imbens, and Kolesar (2012) Clustering, spacial correlations, and randomization inference | 0.511 | 2 | 1 | 100% |
| 5 | Andrews (1991) Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation | 0.405 | 1 | 1 | 100% |
| 6 | Arellano (1987) Computing robust standard errors for within-groups estimators | 0.405 | 1 | 1 | 100% |
| 7 | Bloom (2005) Learning more from social experiments: Evolving analytic approaches | 0.405 | 1 | 1 | 100% |
| 8 | Conley (1999) GMM estimation with cross sectional dependence | 0.405 | 1 | 1 | 100% |
| 9 | Duflo, Glennerster, and Kremer (2007) Using randomization in development economics research: A toolkit | 0.405 | 1 | 1 | 100% |
| 10 | Hansen (2007) Asymptotic properties of a robust variance matrix estimator for panel data when T is large | 0.405 | 1 | 1 | 100% |
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| 4 | Shift-Share Designs in Political Science | 0.000 | 1 | 1 |