Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/59467 
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dc.contributor.authorKim, Hyun Haken
dc.contributor.authorSwanson, Norman R.en
dc.date.accessioned2011-06-14-
dc.date.accessioned2012-06-25T11:57:18Z-
dc.date.available2012-06-25T11:57:18Z-
dc.date.issued2011-
dc.identifier.urihttp://hdl.handle.net/10419/59467-
dc.description.abstractIn this paper, we empirically assess the predictive accuracy of a large group of models based on the use of principle components and other shrinkage methods, including Bayesian model averaging and various bagging, boosting, LASSO and related methods Our results suggest that model averaging does not dominate other well designed prediction model specification methods, and that using a combination of factor and other shrinkage methods often yields superior predictions. For example, when using recursive estimation windows, which dominate other windowing approaches in our experiments, prediction models constructed using pure principal component type models combined with shrinkage methods yield mean square forecast error best models around 70% of the time, when used to predict 11 key macroeconomic indicators at various forecast horizons. Baseline linear models (which win around 5% of the time) and model averaging methods (which win around 25% of the time) fare substantially worse than our sophisticated nonlinear models. Ancillary findings based on our forecasting experiments underscore the advantages of using recursive estimation strategies, and provide new evidence of the usefulness of yield and yield-spread variables in nonlinear prediction specification.en
dc.language.isoengen
dc.publisher|aRutgers University, Department of Economics |cNew Brunswick, NJen
dc.relation.ispartofseries|aWorking Paper |x2011-19en
dc.subject.jelG1en
dc.subject.ddc330en
dc.subject.keywordpredictionen
dc.subject.keywordbaggingen
dc.subject.keywordboostingen
dc.subject.keywordBayesian model averagingen
dc.subject.keywordridge regressionen
dc.subject.keywordleast angle regressionen
dc.subject.keywordelastic net and non-negative garotteen
dc.subject.stwPrognoseverfahrenen
dc.subject.stwStatistische Methodeen
dc.subject.stwWirtschaftsprognoseen
dc.subject.stwKonjunkturprognoseen
dc.subject.stwTheorieen
dc.subject.stwUSAen
dc.titleForecasting financial and macroeconomic variables using data reduction methods: New empirical evidence-
dc.typeWorking Paperen
dc.identifier.ppn662054202en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen
dc.identifier.repecRePEc:rut:rutres:201119en

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