Forecasting gross value-added at the regional level: Are sectoral disaggregated predictions superior to direct ones?
Robert Lehmann () and
Klaus Wohlrabe ()
No 171, ifo Working Paper Series from ifo Institute - Leibniz Institute for Economic Research at the University of Munich
In this paper, we ask whether it is possible to forecast gross value-added (GVA) and its sectoral subcomponents at the regional level. With an autoregressive distributed lag model we forecast total and sectoral GVA for one German state (Saxony) with more than 300 indicators from different regional levels (international, national and regional) and additionally make usage of different forecast pooling strategies and factor models. Our results show that we are able to increase forecast accuracy of GVA for every sector and for all forecast horizons (one up to four quarters) compared to an autoregressive process. Finally, we show that sectoral forecasts contain more information in the short term (one quarter), whereas direct forecasts of total GVA are referable in the medium (two and three quarters) and long term (four quarters).
Keywords: Regional forecasting; gross value-added; forecast combination; disaggregated; forecasts; factor models. (search for similar items in EconPapers)
JEL-codes: C32 C52 C53 E37 R11 (search for similar items in EconPapers)
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Journal Article: Forecasting gross value-added at the regional level: are sectoral disaggregated predictions superior to direct ones? (2014)
Working Paper: Sectoral gross value-added forecasts at the regional level: Is there any information gain? (2013)
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