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Does Noncausality Help in Forecasting Economic Time Series?

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dc.contributor.author Lanne, Markku
dc.contributor.author Nyberg, Henri
dc.contributor.author Saarinen, Erkka
dc.date.accessioned 2017-12-13T14:30:00Z
dc.date.available 2017-12-13T14:30:00Z
dc.date.issued 2012-10
dc.identifier.citation Lanne , M , Nyberg , H & Saarinen , E 2012 , ' Does Noncausality Help in Forecasting Economic Time Series? ' , Economics Bulletin , vol. 32 , no. 4 , pp. 2849-2859 .
dc.identifier.other PURE: 23500418
dc.identifier.other PURE UUID: 9bc73134-72c2-4226-8c98-bc9621bb6cee
dc.identifier.other Scopus: 84873432219
dc.identifier.other ORCID: /0000-0001-9397-2578/work/71510445
dc.identifier.uri http://hdl.handle.net/10138/229523
dc.description.abstract In this paper, we compare the forecasting performance of univariate noncausal and conventional causal autoregressive models for a comprehensive data set consisting of 170 monthly U.S. macroeconomic and financial time series. The noncausal models consistently outperform the causal models. For a collection of quarterly time series, the improvement in forecast accuracy due to allowing for noncausality is found even greater. en
dc.format.extent 11
dc.language.iso eng
dc.relation.ispartof Economics Bulletin
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject 511 Economics
dc.title Does Noncausality Help in Forecasting Economic Time Series? en
dc.type Article
dc.contributor.organization Department of Political and Economic Studies (2010-2017)
dc.contributor.organization Economics
dc.contributor.organization Helsinki Center of Economic Research (HECER) 2010-2012
dc.contributor.organization Financial and Macroeconometrics
dc.description.reviewstatus Peer reviewed
dc.relation.issn 1545-2921
dc.rights.accesslevel openAccess
dc.type.version publishedVersion

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