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dc.contributor.authorWagner, Martin-
dc.contributor.authorGrabarczyk, Peter-
dc.date.accessioned2016-11-23T12:53:27Z-
dc.date.available2016-11-23T12:53:27Z-
dc.date.issued2016-
dc.identifier.urihttp://hdl.handle.net/2003/35391-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-17432-
dc.description.abstractWe present estimation and inference techniques for systems of seemingly unrelated cointegrating polynomial regressions. In particular, we present two fully modified-type estimators and Wald-type hypothesis tests based upon them. We develop tests for poolability of subsets of coefficients over subsets of equations. For the case that these restrictions are not rejected, we provide the correspondingly pooled estimators. This group-wise pooling turns out to be very useful in our application where we analyze the environmental Kuznets curve for CO2 emissions for seven early industrialized countries. Group-wise pooled estimation leads to almost the same results as unrestricted estimation whilst reducing the number of estimated parameters by about one third. Fully pooled, panel-data type estimation performs poorly in comparison.en
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB823;75, 2016en
dc.subjectcointegrating polynomial regressionen
dc.subjectseemingly unrelated regressionen
dc.subjectpoolabilityen
dc.subjectfully modified estimationen
dc.subjectenvironmental Kuznets curveen
dc.subject.ddc310-
dc.subject.ddc330-
dc.subject.ddc620-
dc.titleThe environmental Kuznets curve for carbon dioxide emissions: A seemingly unrelated cointegrating polynomial regressions approachen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access-
Appears in Collections:Sonderforschungsbereich (SFB) 823

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