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dc.contributor.authorDeckers, Thomasen_US
dc.contributor.authorHanck, Christophen_US
dc.date.accessioned2010-08-11T09:12:05Z-
dc.date.available2010-08-11T09:12:05Z-
dc.date.issued2010en_US
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_US
dc.language.isoengen_US
dc.publisherVerein für Socialpolitik Frankfurt a. M.en_US
dc.relation.ispartofseriesBeiträge zur Jahrestagung des Vereins für Socialpolitik 2010: Ökonomie der Familie - Session: Analysing Macroeconomic Panel Data Sets B2-V3en_US
dc.subject.jelO47en_US
dc.subject.jelC12en_US
dc.subject.jelC33en_US
dc.subject.ddc330en_US
dc.subject.keywordGrowth Empiricsen_US
dc.subject.keywordPanel Dataen_US
dc.subject.keywordMultiple Testingen_US
dc.subject.keywordConvergenceen_US
dc.subject.keywordBootstrapen_US
dc.titleMultiple Testing in Growth Econometricsen_US
dc.typeConference Paperen_US
dc.identifier.ppn654580588-
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
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