Persistence, Randomization, and Spatial Noise
|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 persistence; Spatial data; Randomization inference; Spatial noise||JEL Codes:||N0||Language:||en||Status of Item:||Not peer reviewed||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by-nc-nd/3.0/ie/|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|
Show full item record
If you are a publisher or author and have copyright concerns for any item, please email email@example.com and the item will be withdrawn immediately. The author or person responsible for depositing the article will be contacted within one business day.