Morgan Kelly
The Standard Errors of Persistence
large literature persistence finds many modern outcomes strongly reflect characteristics places distant past however alongside unusually high statistics regressions display severe spatial autocorrelation residuals purpose paper examine whether two properties might connected start running artificial regressions variables spatial noise find even modest ranges spatial correlation points statistics become severely inflated leading significance levels error several orders magnitude analyse 27 persistence studies leading journals find cases replace main explanatory variable spatial noise fit regression commonly improves replace dependent variable spatial noise persistence variable still explain high significance levels predict advance persistence results might outcome fitting spatial noise degree spatial autocorrelation residuals measured standard moran statistic findings suggest results persistence studies spatial regressions generally might treated caution absence reported moran statistics noise simulations