While practitioners often have recourse to bridge equations to monitor the latest developments in the economic situation, econometric evidence about the forecast robustness of leading indicators models is scarce. Leading indicators are noisy and their forecast properties are unstable since they contain shock-specific information. There are an infinite number of combinations of potential leading indicators but no simple rule to select the most appropriate ones. Factor analysis can partially alleviate difficulties on both grounds: indexes derived from factors are cleansed of some of the noise or idiosyncrasies and are less subject to the problem of model uncertainty. This paper introduced a factor model, based on a large number of series sorted according to their cross-correlation with GDP, which can be used to perform forecasts up to two quarters ahead.
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