The standard errors of persistence

IF 3.8 1区经济学 Q1 ECONOMICS

Journal of International Economics Pub Date : 2024-11-26 DOI:10.1016/j.jinteco.2024.104027

Timothy G. Conley, Morgan Kelly

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Abstract

Many studies of historical persistence find that modern outcomes strongly reflect characteristics of the same places in the distant past. However they rely on data that often exhibit extreme spatial trends and autocorrelation, suggesting that their unusually large t-statistics may be due to inadequately controlling for spurious correlation. To analyze this we introduce a new regression procedure and two diagnostic tests of no treatment effect: (a) a placebo test where the treatment is replaced with spatial noise and (b) a synthetic outcomes test of the hypothesis that the outcome is generated by a trend plus a spatial noise process independent of the treatment. We then show how reliable regression results can be obtained by adding a low dimensional spatial basis to the regression of interest, and applying a large cluster standard error correction. Examining 30 persistence studies in leading journals we find that few approach significance at conventional levels. Our procedure applies to regressions with spatial observations more generally and is implemented in an open source package.

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来源期刊

Journal of International Economics ECONOMICS-

CiteScore

5.80

自引率

6.10%

发文量

期刊介绍： The Journal of International Economics is intended to serve as the primary outlet for theoretical and empirical research in all areas of international economics. These include, but are not limited to the following: trade patterns, commercial policy; international institutions; exchange rates; open economy macroeconomics; international finance; international factor mobility. The Journal especially encourages the submission of articles which are empirical in nature, or deal with issues of open economy macroeconomics and international finance. Theoretical work submitted to the Journal should be original in its motivation or modelling structure. Empirical analysis should be based on a theoretical framework, and should be capable of replication.