Spatial Correlation Robust Inference.
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....
| Published in: | Econometrica Vol. 90; no. 6; pp. 2901 - 2936 |
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| Main Authors: | , |
| Format: | Article |
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Wiley-Blackwell
Nov2022
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=160352058&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 160352058 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00129682 ECN jtl: Econometrica issn: 00129682 maglogo: Y pubinfo: dt: Nov2022 vid: 90 iid: 6 pid: 480 pub: Wiley-Blackwell artinfo: ui: 160352058 10.3982/ECTA19465 ppf: 2901 ppct: 35 formats: tig: atl: Spatial Correlation Robust Inference. aug: au: Müller, Ulrich K. Watson, Mark W. affil: Department of Economics, Princeton University su: Confidence intervals Sample size (Statistics) Units of time Parametric modeling Random fields sug: subj: Confidence intervals Sample size (Statistics) Units of time Parametric modeling Random fields keyword: Confidence interval HAC HAR random field Confidence interval HAC HAR random field ab: 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 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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