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dc.contributor.authorCarriero, Andreaen
dc.contributor.authorKapetanios, Georgeen
dc.contributor.authorMarcellino, Massimilianoen
dc.date.accessioned2010-08-04-
dc.date.accessioned2012-02-09T14:06:17Z-
dc.date.available2012-02-09T14:06:17Z-
dc.date.issued2010-
dc.identifier.urihttp://hdl.handle.net/10419/55155-
dc.description.abstractWe propose a new approach to forecasting the term structure of interest rates, which allows to efficiently extract the information contained in a large panel of yields. In particular, we use a large Bayesian Vector Autoregression (BVAR) with an optimal amount of shrinkage towards univariate AR models. Focusing on the U.S., we provide an extensive study on the forecasting performance of our proposed model relative to most of the existing alternative specifications. While most of the existing evidence focuses on statistical measures of forecast accuracy, we also evaluate the performance of the alternative forecasts when used within trading schemes or as a basis for portfolio allocation. We extensively check the robustness of our results via subsample analysis and via a data based Monte Carlo simulation. We find that: i) our proposed BVAR approach produces forecasts systematically more accurate than the random walk forecasts, though the gains are small; ii) some models beat the BVAR for a few selected maturities and forecast horizons, but they perform much worse than the BVAR in the remaining cases; iii) predictive gains with respect to the random walk have decreased over time; iv) different loss functions (i.e., statistical vs economic) lead to different ranking of specific models; v) modelling time variation in term premia is important and useful for forecasting.en
dc.language.isoengen
dc.publisher|aQueen Mary University of London, School of Economics and Finance |cLondonen
dc.relation.ispartofseries|aWorking Paper |x662en
dc.subject.jelC11en
dc.subject.jelC53en
dc.subject.jelE43en
dc.subject.jelE47en
dc.subject.ddc330en
dc.subject.keywordBayesian methodsen
dc.subject.keywordforecastingen
dc.subject.keywordterm structureen
dc.subject.stwPrognoseverfahrenen
dc.subject.stwBayes-Statistiken
dc.subject.stwVAR-Modellen
dc.subject.stwZinsstrukturen
dc.titleForecasting government bond yields with large Bayesian VARs-
dc.typeWorking Paperen
dc.identifier.ppn63222813Xen
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

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