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The paper introduces an approximate dynamic factor model based on the extraction of principal components from a very large number of leading indicators stacked at various lags. The model is designed to produce short-term forecasts that are computed with the EM algorithm implemented with the first few eigenvectors ordered by descending eigenvalues. A cross-sectional bootstrap experiment is used to shed light on the sensitivity of the factor model to factor selection and to sampling uncertainty. The empirical number of factors seems more appropriately set through an analysis of eigenvalues, bootstrapped eigenvalues or the BIC than with more sophisticated information criteria. Confidence intervals derived from bootstrapped forecasts show the extent to which the data composition can support the hypothesis of business cycle co-movements and the selected factors can account for those shocks. Pseudo real-time out-of-sample forecast experiments conducted with a dataset of about two thousand series covering the euro area business cycle show that the SLID factor model outperforms benchmark models (AR models, leading indicators equations) for one-, two- and three- quarters-ahead forecasts of GDP growth. The accuracy of coincident forecasts compared to final estimates is not significantly different from Eurostat Flash or first estimates and is slightly superior to that of CEPR Eurocoin.
