Understanding Persistence

Files in This Item:
File Description SizeFormat 
WP20_23.pdf1.41 MBAdobe PDFDownload
Title: Understanding Persistence
Authors: Kelly, Morgan
Permanent link: http://hdl.handle.net/10197/11538
Date: Sep-2020
Online since: 2020-09-08T10:33:50Z
Abstract: A large literature on persistence finds that many modern outcomes strongly reflect characteristics of the same places in the distant past. These studies typically combine unusually high t statistics with severe spatial autocorrelation in residuals, suggesting that some findings may be artefacts of underestimating standard errors or of fitting spatial trends. For 25 studies in leading journals, I apply three basic robustness checks against spatial trends and find that effect sizes typically fall by over half, leaving most well known results insignificant at conventional levels. Turning to standard errors, there is currently no data-driven method for selecting an appropriate HAC spatial kernel. The paper proposes a simple procedure where a kernel with a highly flexible functional form is estimated by maximum likelihood. After correction, standard errors tend to rise substantially for cross sectional studies but to fall for panels. Overall, credible identification strategies tend to perform no better than naive regressions. Although the focus here is on historical persistence, the methods apply to regressions using spatial data more generally.
Type of material: Working Paper
Publisher: University College Dublin. School of Economics
Start page: 1
End page: 50
Series/Report no.: UCD Centre for Economic Research Working Paper Series; WP2020/23
Copyright (published version): 2020 the Author
Keywords: Deep originsRobustness checksSpatial noiseExplanatory variablesStandard errors
Language: en
Status of Item: Not peer reviewed
Appears in Collections:Economics Working Papers & Policy Papers

Show full item record

Page view(s)

35
Last Week
9
Last month
checked on Sep 30, 2020

Download(s)

3
checked on Sep 30, 2020

Google ScholarTM

Check


This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.