Is spatial bootstrapping a panacea for valid inference?

  • Bootstrapping methods have so far been rarely used to evaluate spatial data sets. Based on an extensive Monte Carlo study we find that also for spatial, cross-sectional data, the wild bootstrap test proposed by Davidson and Flachaire (2008) based on restricted residuals clearly outperforms asymptotic as well as competing bootstrap tests, like the pairs bootstrap.

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Metadaten
Author:Torben Klarl
URN:urn:nbn:de:bvb:384-opus4-710952
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/71095
Series (Serial Number):Volkswirtschaftliche Diskussionsreihe (322)
Publisher:Volkswirtschaftliches Institut, Universität Augsburg
Place of publication:Augsburg
Type:Working Paper
Language:English
Year of first Publication:2013
Publishing Institution:Universität Augsburg
Release Date:2020/02/20
Tag:JEL: C18, C21, R11
Pagenumber:19
Institutes:Wirtschaftswissenschaftliche Fakultät
Wirtschaftswissenschaftliche Fakultät / Institut für Volkswirtschaftslehre
Dewey Decimal Classification:3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft
Journals:Volkswirtschaftliche Diskussionsreihe
Licence (German):Deutsches Urheberrecht