Forecasting gross value-added at the regional level: are sectoral disaggregated predictions superior to direct ones?
Robert Lehmann () and
Klaus Wohlrabe ()
Review of Regional Research: Jahrbuch für Regionalwissenschaft, 2014, vol. 34, issue 1, 61-90
In this paper, we ask whether it is possible to forecast gross value-added (GVA) and its sectoral sub-components 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 several 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 preferable in the medium (two and three quarters) and long term (four quarters). Copyright Springer-Verlag Berlin Heidelberg 2014
Keywords: Regional forecasting; Gross value-added; Forecast combination; Disaggregated forecasts; Factor models; C32; C52; C53; E37; R11 (search for similar items in EconPapers)
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Working Paper: Forecasting gross value-added at the regional level: Are sectoral disaggregated predictions superior to direct ones? (2013)
Working Paper: Sectoral gross value-added forecasts at the regional level: Is there any information gain? (2013)
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