Sectoral gross value-added forecasts at the regional level: Is there any information gain?

Robert Lehmann and Klaus Wohlrabe

MPRA Paper from University Library of Munich, Germany

Abstract: In this paper, we ask whether it is possible to forecast gross-value added (GVA) and its sectoral sub-components at the regional level. We are probably the first who evaluate sectoral forecasts at the regional level using a huge data set at quarterly frequency to investigate this issue. With an autoregressive distributed lag model we forecast total and sectoral GVA for one of the German states (Saxony) with more than 300 indicators from different regional levels (international, national and regional) and additionally make usage of different pooling strategies. Our results show that we are able to increase forecast accuracy of GVA for every sector and for all forecast horizons 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 preferable in the medium (two and three quarters) and long term (four quarters).

Keywords: regional forecasting; gross value added; leading indicators; forecast combination; disaggregated forecasts (search for similar items in EconPapers)
JEL-codes: C32 C52 C53 E37 R11 (search for similar items in EconPapers)
Date: 2013-05-06
New Economics Papers: this item is included in nep-for and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Related works:
Journal Article: Forecasting gross value-added at the regional level: are sectoral disaggregated predictions superior to direct ones? (2014)
Working Paper: Forecasting gross value-added at the regional level: Are sectoral disaggregated predictions superior to direct ones? (2013)
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