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dc.contributor.authorBANERJEE, Anindyaen
dc.contributor.authorMARCELLINO, Massimilianoen
dc.identifier.citationInternational Journal of Forecasting, 2006, 22, 1, 137-151en
dc.description.abstractIn this paper, we evaluate the relative merits of three alternative approaches to extracting information from a large data set for forecasting, namely, the use of an automated model selection procedure, the adoption of a factor model, and the adoption of single-indicator-based forecast pooling. The comparison is conducted using a large set of indicators for forecasting US inflation and GDP growth. We also compare our large set of leading indicators with purely autoregressive models, using an evaluation procedure that is particularly relevant for policy making. The evaluation is conducted both ex post and in a pseudo-real-time context, for several forecast horizons, and using both recursive and rolling estimation. The results indicate a preference for simple forecasting tools, with a good relative performance of pure autoregressive models, and substantial instability in the characteristics of the leading indicators. A pseudo real-time analysis provides a useful guide to the selection of the best leading indicator, in particular for GDP growth.en
dc.relation.ispartofInternational Journal of Forecasting
dc.titleAre there Any Reliable Leading Indicators for US Inflation and GDP growth?en

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