Panel cointegrating polynomial regression analysis and the environmental Kuznets curve

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dc.contributor.authorde Jong, Robert M.
dc.contributor.authorWagner, Martin
dc.date.accessioned2018-10-10T13:23:00Z
dc.date.available2018-10-10T13:23:00Z
dc.date.issued2018
dc.description.abstractThis paper develops a modified and a fully modified OLS estimator for a panel of cointegrating polynomial regressions, i.e. regressions that include an integrated process and its powers as explanatory variables. The stationary errors are allowed to be serially correlated and the regressors are allowed to be endogenous and we allow for individual and time fixed effects. Inspired by Phillips and Moon (1999) we consider a cross-sectional i.i.d. random linear process framework. The modified OLS estimator utilizes the large cross-sectional dimension that allows to consistently estimate and subtract an additive bias term without the need to also transform the dependent variable as required in fully modified OLS estimation. Both developed estimators have zero mean Gaussian limiting distributions and thus allow for standard asymptotic inference. Our illustrative application indicates that the developed methods are a potentially useful addition to not least the environmental Kuznets curve literature's toolkit.en
dc.identifier.urihttp://hdl.handle.net/2003/37148
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-19144
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB823;22/2018en
dc.subjectcointegrationen
dc.subjectunit rootsen
dc.subjectpolynomial transformationen
dc.subjectpanel dataen
dc.subjectenvironmental Kuznets curve,en
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.subject.rswkKointegrationde
dc.subject.rswkKuznets-Kurvede
dc.subject.rswkRegressionsanalysede
dc.subject.rswkMethode der kleinsten Quadratede
dc.titlePanel cointegrating polynomial regression analysis and the environmental Kuznets curveen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access
eldorado.secondarypublicationfalsede

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