Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/37534 
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dc.contributor.authorDeckers, Thomasen
dc.contributor.authorHanck, Christophen
dc.date.accessioned2010-08-11T09:12:05Z-
dc.date.available2010-08-11T09:12:05Z-
dc.date.issued2010-
dc.identifier.urihttp://hdl.handle.net/10419/37534-
dc.description.abstractThis 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 in panel data sets of 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 in panels 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.en
dc.language.isoengen
dc.publisher|aVerein für Socialpolitik |cFrankfurt a. M.en
dc.relation.ispartofseries|aBeiträge zur Jahrestagung des Vereins für Socialpolitik 2010: Ökonomie der Familie - Session: Analysing Macroeconomic Panel Data Sets |xB2-V3en
dc.subject.jelO47en
dc.subject.jelC12en
dc.subject.jelC33en
dc.subject.ddc330en
dc.subject.keywordGrowth Empiricsen
dc.subject.keywordPanel Dataen
dc.subject.keywordMultiple Testingen
dc.subject.keywordConvergenceen
dc.subject.keywordBootstrapen
dc.titleMultiple Testing in Growth Econometrics-
dc.typeConference Paperen
dc.identifier.ppn654580588en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

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