{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T22:36:21Z","timestamp":1790634981611,"version":"4.1.0"},"reference-count":46,"publisher":"The Econometric Society","issue":"6","funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ECTA"],"abstract":"<jats:p>We propose a method for constructing confidence intervals that account for many forms of spatial correlation. The interval has the familiar \u201cestimator plus and minus a standard error times a critical value\u201d 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 \u201cworst\u2010case\u201d spatial correlation 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 finite sample Gaussian settings in a restricted but nonparametric class of models and in large samples whenever the spatial correlation is weak, that is, with average pairwise correlations that vanish as the sample size gets large. We also provide results on the efficiency of the method.<\/jats:p>","DOI":"10.3982\/ecta19465","type":"journal-article","created":{"date-parts":[[2022,11,14]],"date-time":"2022-11-14T08:47:18Z","timestamp":1668415638000},"page":"2901-2935","source":"Crossref","is-referenced-by-count":42,"title":["Spatial Correlation Robust Inference"],"prefix":"10.3982","volume":"90","author":[{"given":"Ulrich K.","family":"M\u00fcller","sequence":"first","affiliation":[{"name":"Department of Economics, Princeton University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark W.","family":"Watson","sequence":"additional","affiliation":[{"name":"Department of Economics, Princeton 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