Ruonan Xu, Jeffrey M. Wooldridge
arXiv 25 Nov 2022 · Econometrics
arXiv:2211.14354 · PDF · DOI · OpenAlex · Extracted main text
When observing spatial data, what standard errors should we report? With the finite population framework, we identify three channels of spatial correlation: sampling scheme, assignment design, and model specification. The Eicker-Huber-White standard error, the cluster-robust standard error, and the spatial heteroskedasticity and autocorrelation consistent standard error are compared under different combinations of the three channels. Then, we provide guidelines for whether standard errors should be adjusted for spatial correlation for both linear and nonlinear estimators. As it turns out, the answer to this question also depends on the magnitude of the sampling probability.
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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 | Jenish, N. and Prucha, I.R (2012) On spatial processes and asymptotic inference under near-epoch dependence | 0.794 | 6 | 3 | 50% |
| 2 | Bradley, R.C. and Tone, C (2017) A central limit theorem for non-stationary strongly mixing random fields | 0.693 | 6 | 3 | 33% |
| 3 | Leung, M.P (2022) Causal inference under approximate neighborhood interference | 0.644 | 2 | 2 | 100% |
| 4 | Sävje, F (2021) Causal inference with misspecified exposure mappings | 0.644 | 2 | 2 | 100% |
| 5 | Gallant, A.R. and White, H (1988) A unified theory of estimation and inference for nonlinear dynamic models | 0.585 | 5 | 3 | 20% |
| 6 | Abadie, A., Athey, S., Imbens, G.W., and Wooldridge, J.M (2020) Sampling-based versus design-based uncertainty in regression analysis self | 0.585 | 3 | 1 | 100% |
| 7 | Jenish, N. and Prucha, I.R (2009) Central limit theorems and uniform laws of large numbers for arrays of random fields | 0.511 | 4 | 2 | 25% |
| 8 | Abadie, A., Athey, S., Imbens, G.W., and Wooldridge, J (2017) When should you adjust standard errors for clustering? Tech self | 0.405 | 1 | 1 | 100% |
| 9 | Aliprantis, D. and Hartley, D (2015) Blowing it up and knocking it down: The local and city-wide effects of demolishing high concentration public housing on crime | 0.405 | 1 | 1 | 100% |
| 10 | Bojinov, I., Rambachan, A., and Shephard, N (2021) Panel experiments and dynamic causal effects: A finite population perspective | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 25 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Clustering with Potential Multidimensionality: Inference and Practice | 0.405 | 1 | 1 |
| 2 | Finite Population Identification and Design-Based Sensitivity Analysis | 0.405 | 1 | 1 |