Multiple Testing Techniques in Growth Econometrics

Thomas Deckers and Christoph Hanck

MPRA Paper from University Library of Munich, Germany

Abstract: This paper discusses two longstanding questions in growth econometrics which involve multiple hypothesis testing. In cross sectional GDP growth regressions many variables are simultaneously tested for significance. Similarly, when investigating pairwise convergence of output for $n$ countries, $n(n-1)/2$ tests are performed. We propose to control the false discovery rate (FDR) so as not to erroneously declare variables significant in these multiple testing situations only because of the large number of tests performed. Doing so, we provide a simple new way to robustly select variables in economic growth models. We find that few other variables beyond the initial GDP level are needed to explain growth. We also show that convergence of per capita output using a time series definition with the necessary condition of no unit root in the log per-capita output gap of two economies does not appear to hold

Keywords: Growth Empirics; Multiple Testing; Convergence; Bootstrap (search for similar items in EconPapers)
JEL-codes: C12 O47 (search for similar items in EconPapers)
Date: 2009-10-10
New Economics Papers: this item is included in nep-ecm and nep-fdg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:17843

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