Discussion paper

DP16609 Persistence, Randomization, and Spatial Noise

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. The performance of twenty persistence studies relative to spatial noise is examined.


Kelly, M (2021), ‘DP16609 Persistence, Randomization, and Spatial Noise‘, CEPR Discussion Paper No. 16609. CEPR Press, Paris & London. https://cepr.org/publications/dp16609