Persistence, Randomization, and Spatial Noise (revised paper)

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Title: Persistence, Randomization, and Spatial Noise (revised paper)
Authors: Kelly, Morgan
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Date: Nov-2021
Online since: 2022-02-28T16:15:46Z
Abstract: Historical persistence studies and other regressions using spatial data commonly return severely inflated t statistics, and different standard error estimates that attempt to correct for this vary so widely as to be as to be of limited use in practice. 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. Examining twenty persistence studies, few perform substantially above the level of spatial noise.
Type of material: Working Paper
Publisher: University College Dublin. School of Economics
Start page: 1
End page: 38
Series/Report no.: UCD Centre for Economic Research Working Paper Series; WP2021/25
Copyright (published version): 2021 the Author
Keywords: Spatial noiseStandard errorRandomization inferenceExchangeable observationsHIstorical persistence
JEL Codes: N0
Language: en
Status of Item: Not peer reviewed
This item is made available under a Creative Commons License:
Appears in Collections:Economics Working Papers & Policy Papers

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