Persistence, Randomization, and Spatial Noise

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Title: Persistence, Randomization, and Spatial Noise
Authors: Kelly, Morgan
Permanent link: http://hdl.handle.net/10197/12565
Date: Oct-2021
Online since: 2021-10-19T11:43:17Z
Abstract: Historical 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.
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; WP2021/24
Copyright (published version): 2021 the Author
Keywords: Historical persistenceSpatial dataRandomization inferenceSpatial noise
JEL Codes: N0
Language: en
Status of Item: Not peer reviewed
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:Economics Working Papers & Policy Papers

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