Espasa, AntoniAlbacete, Rebeca2008-11-172011-06-072008-11-172011-06-072007-08Journal of Forecasting, (August 2007), v. 26, n. 5, p. 303-3160277-6693http://hdl.handle.net/10016/3136This paper examines the problem of forecasting macro-variables which are observed monthly (or quarterly) and result from geographical and sectorial aggregation. The aim is to formulate a methodology whereby all relevant information gathered in this context could provide more accurate forecasts, be frequently updated, and include a disaggregated explanation as useful information for decision-making. The appropriate treatment of the resulting disaggregated data set requires vector modelling, which captures the long-run restrictions between the different time series and the short-term correlations existing between their stationary transformations. Frequently, due to a lack of degrees of freedom, the vector model must be restricted to a block-diagonal vector model. This methodology is applied in this paper to inflation in the euro area, and shows that disaggregated models with cointegration restrictions improve accuracy in forecasting aggregate macro-variables. Copyright © 2007 John Wiley & Sons, Ltd.application/pdftext/plainengsectorial and geographical disaggregationVEqCMcointegrationcore inflationcombination of forecastsresearch articleEstadística10.1002/for.1021open access