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In the construction of a leading indicator model of economic activity, economists must select among a pool of variables which lead output growth. Usually the pool of variables is large and a selection of a subset must be carried out. This paper proposes an automatic leading indicator model which, rather than preselection, uses a dynamic factor model to summarize the information content of a pool of variables. Results using quarterly data for France, Germany, Italy and the United Kingdom show that the overall forecasting performance of the automatic leading indicator model appears better than that of more traditional VAR and BVAR models.