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A practitioner approach to forecasting cross section and aggregated outcomes

The aim of this work is to show how the recent addenda to the DFM approach toolkit can be used in empirical analysis and, starting from this base, forecast at the desired level of disaggregation some or all the series that compose the dataset.

This approach is intended to satisfy the need of making a bridge between macro/aggregated forecasts and micro/disaggregated outcome. In applied economics, this is often done in steps, in which firstly an aggregated forecast is produced and then, on its basis, the disaggregated variables (in terms of sectors, regions or firms specific data) are produced, with the constraint of summing-up in some way to the pre-estimated total. In the proposed exercise the common dynamic factors are identified, by means of a sign restriction approach.

They are “named” on the basis of their role in the economy and hence a projection is made on their possible development (just like is done in scenario analysis for the exogenous variables in a macro model). Using the projected shocks a forecast on the common components of all of the dataset’s variables could be, in principle, extracted. A Box-Jenkins style forecast is added for the idiosyncratic part; these two parts are then combined, according to their relative weight (due to the co-movements with the whole dataset).

The obtained disaggregated forecasts preserve the coherence with the aggregate scenario, whose character is implicit in the projection on fundamental factor.

http://unicreditanduniversities.eu/uploads/assets/WP_2010/WorkingPaper_n4_Brasili.pdf

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