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dc.contributor.authorMARCELLINO, Massimiliano
dc.date.accessioned2016-07-26T15:10:50Z
dc.date.available2016-07-26T15:10:50Z
dc.date.issued2007
dc.identifier.citationJournal of time series analysis, 2007, Vol. 28, No. 1, pp. 53-71
dc.identifier.issn1467-9892
dc.identifier.urihttps://hdl.handle.net/1814/42718
dc.description.abstractPooling forecasts obtained from different procedures typically reduces the mean square forecast error and more generally improve the quality of the forecast. In this paper, we evaluate whether pooling-interpolated or-backdated time series obtained from different procedures can also improve the quality of the generated data. Both simulation results and empirical analyses with macroeconomic time series indicate that pooling plays a positive and important role in this context also.
dc.language.isoen
dc.relation.ispartofJournal of time series analysis
dc.subjectPooling
dc.subjectInterpolation
dc.subjectFactor model
dc.subjectKalman filter
dc.subjectspline
dc.subjectC32
dc.subjectC43
dc.subjectC82
dc.titlePooling-based data interpolation and backdating
dc.typeArticle
dc.identifier.doi10.1111/j.1467-9892.2006.00498.x
dc.identifier.volume28
dc.identifier.startpage53
dc.identifier.endpage71
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dc.identifier.issue1


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