Persistence, Randomization, and Spatial Noise

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dc.contributor.authorKelly, Morgan-
dc.date.accessioned2021-10-19T11:43:17Z-
dc.date.available2021-10-19T11:43:17Z-
dc.date.copyright2021 the Authoren_US
dc.date.issued2021-10-
dc.identifier.other202124-
dc.identifier.urihttp://hdl.handle.net/10197/12565-
dc.description.abstractHistorical persistence studies and other regressions using spatial data commonly have severely inflated t statistics, and different standard error adjustments to correct for this return markedly different estimates. This paper proposes a simple randomization inference procedure where the significance level of an explanatory variable is measured by its ability to outperform synthetic noise with the same estimated spatial structure. Spatial noise, in other words, acts as a treatment randomization in an artificial experiment based on correlated observational data. Combined with Müller and Watson (2021), randomization gives a way to estimate credible confidence intervals for spatial regressions. The performance of twenty persistence studies relative to spatial noise is examined.en_US
dc.language.isoenen_US
dc.publisherUniversity College Dublin. School of Economicsen_US
dc.relation.ispartofseriesUCD Centre for Economic Research Working Paper Seriesen_US
dc.relation.ispartofseriesWP2021/24en_US
dc.subjectHistorical persistenceen_US
dc.subjectSpatial dataen_US
dc.subjectRandomization inferenceen_US
dc.subjectSpatial noiseen_US
dc.subject.classificationN0en_US
dc.titlePersistence, Randomization, and Spatial Noiseen_US
dc.typeWorking Paperen_US
dc.statusNot peer revieweden_US
dc.identifier.startpage1en_US
dc.identifier.endpage38en_US
dc.neeo.contributorKelly|Morgan|aut|-
dc.rights.licensehttps://creativecommons.org/licenses/by-nc-nd/3.0/ie/en_US
item.grantfulltextopen-
item.fulltextWith Fulltext-
Appears in Collections:Economics Working Papers & Policy Papers
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