Ulrich K. Müller, Mark W. Watson
arXiv 18 Feb 2021 · Econometrics · publishedEconometrica (2022) · 36 citations (OpenAlex)
arXiv:2102.09353 · PDF · DOI · OpenAlex · Extracted main text
We propose a method for constructing confidence intervals that account for many forms of spatial correlation. The interval has the familiar `estimator plus and minus a standard error times a critical value' form, but we propose new methods for constructing the standard error and the critical value. The standard error is constructed using population principal components from a given `worst-case' spatial covariance model. The critical value is chosen to ensure coverage in a benchmark parametric model for the spatial correlations. The method is shown to control coverage in large samples whenever the spatial correlation is weak, i.e., with average pairwise correlations that vanish as the sample size gets large. We also provide results on correct coverage in a restricted but nonparametric class of strong spatial correlations, as well as on the efficiency of the method. In a design calibrated to match economic activity in U.S. states the method outperforms previous suggestions for spatially robust inference about the population mean.
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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 | Dou (2019) Optimal HAR Inference | 1.000 | 5 | 4 | 100% |
| 2 | Sun and Kim (2012) Asymptotic F-Test in a GMM Framework with Cross-Sectional Dependence | 1.000 | 5 | 4 | 100% |
| 3 | Conley (1999) GMM Estimation with Cross Sectional Dependence | 0.843 | 3 | 3 | 100% |
| 4 | Lahiri (2003) Central Limit Theorems for Weighted Sums of a Spatial Process under a Class of Stochastic and Fixed Designs | 0.822 | 6 | 2 | 83% |
| 5 | Bester, Conley, Hansen, and Vogelsang (2016) Fixed-b Asymptotics for Spatially Dependent Robust Nonparametric Covariance Matrix Estimators | 0.737 | 3 | 2 | 100% |
| 6 | Ibragimov and Müller (2010) T-Statistic Based Correlation and Heterogeneity Robust Inference | 0.737 | 3 | 2 | 100% |
| 7 | Kiefer, Vogelsang, and Bunzel (2000) Simple Robust Testing of Regression Hypotheses | 0.644 | 2 | 2 | 100% |
| 8 | Kiefer and Vogelsang (2005) A New Asymptotic Theory for Heteroskedasticity-Autocorrelation Robust Tests | 0.644 | 2 | 2 | 100% |
| 9 | Lazarus, Lewis, Stock, and Watson (2018) HAR Inference: Recommendations for Practice | 0.644 | 2 | 2 | 100% |
| 10 | Müller (2004) A Theory of Robust Long-Run Variance Estimation | 0.644 | 2 | 2 | 100% |
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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.