 Density Forecast TransformationsMatteo Mogliani and Florens Odendahl Working papers from Banque de France Abstract: The popular choice of using a direct forecasting scheme implies that the individual predictions ignore information on their cross-horizon dependence. However, this dependence is needed if the forecaster has to construct, based on direct density forecasts, predictive objects that are functions of several horizons (e.g. when constructing annual-average growth rates from quarter-on-quarter growth rates). To address this issue, we propose to use copulas to combine the individual h-step-ahead predictive distributions into a joint predictive distribution. Our method is particularly appealing to practitioners for whom changing the direct forecasting specification is too costly. In a Monte Carlo study, we demonstrate that our approach leads to a better approximation of the true density than an approach which ignores the potential dependence. We show the superior performance of our method in several empirical examples, where we construct (i) quarterly forecasts using month-on-month direct forecasts, (ii) annual-average forecasts using monthly year-on-year direct forecasts, and (iii) annual-average forecasts using quarter-on-quarter direct forecast. Keywords: Joint Predictive Distribution; Frequency Transformation; Path Forecasts; Cross-horizon Dependence (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bfr:banfra:1027 Access Statistics for this paper More papers in Working papers from Banque de France Banque de France 31 Rue Croix des Petits Champs LABOLOG - 49-1404 75049 PARIS. Contact information at EDIRC. |
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