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dc.contributor.authorKasparis, Ioannisen
dc.contributor.authorAndreou, Elenaen
dc.contributor.authorPhillips, Peter C. B.en
dc.creatorKasparis, Ioannisen
dc.creatorAndreou, Elenaen
dc.creatorPhillips, Peter C. B.en
dc.date.accessioned2019-05-03T05:22:21Z
dc.date.available2019-05-03T05:22:21Z
dc.date.issued2015
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/47473
dc.description.abstractA unifying framework for inference is developed in predictive regressions where the predictor has unknown integration properties and may be stationary or nonstationary. Two easily implemented nonparametric F-tests are proposed. The limit distribution of these predictive tests is nuisance parameter free and holds for a wide range of predictors including stationary as well as non-stationary fractional and near unit root processes. Asymptotic theory and simulations show that the proposed tests are more powerful than existing parametric predictability tests when deviations from unity are large or the predictive regression is nonlinear. Empirical illustrations to monthly SP500 stock returns data are provided. © 2014 Elsevier B.V. All rights reserved.en
dc.language.isoengen
dc.sourceJournal of Econometricsen
dc.subjectRegression analysisen
dc.subjectStock returnsen
dc.subjectFractional Ornstein hlenbeck processen
dc.subjectFunctional regressionen
dc.subjectInvestmentsen
dc.subjectNon-parametricen
dc.subjectNon-parametric regressionen
dc.subjectNonparametric predictability testen
dc.subjectNonparametric regressionen
dc.subjectPredictive regressioen
dc.titleNonparametric predictive regressionen
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.1016/j.jeconom.2014.05.015
dc.description.volume185
dc.description.startingpage468
dc.description.endingpage494
dc.author.facultyΣχολή Οικονομικών Επιστημών και Διοίκησης / Faculty of Economics and Management
dc.author.departmentΤμήμα Οικονομικών / Department of Economics
dc.type.uhtypeArticleen
dc.contributor.orcidKasparis, Ioannis [0000-0002-9792-4183]
dc.description.totalnumpages468-494


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