Persistence, Randomization, and Spatial Noise

Morgan Kelly

No 202124, Working Papers from School of Economics, University College Dublin

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.

Keywords: Historical persistence; Spatial data; Randomization inference; Spatial noise (search for similar items in EconPapers)
JEL-codes: N0 (search for similar items in EconPapers)
Pages: 38 pages
Date: 2021-10
New Economics Papers: this item is included in nep-ecm, nep-geo, nep-his and nep-ure
References: View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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http://hdl.handle.net/10197/12565 First version, 2021 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:ucn:wpaper:202124

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