Forecasting gross value-added at the regional level: Are sectoral disaggregated predictions superior to direct ones?
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 lagmodel 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).
Year of publication: |
2013
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Authors: | Lehmann, Robert ; Wohlrabe, Klaus |
Institutions: | ifo Leibniz-Institut für Wirtschaftsforschung an der Universität München e.V. |
Subject: | Regional forecasting | gross value-added | forecast combination | disaggregated forecasts | factor models |
Saved in:
freely available
Extent: | application/pdf |
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Series: | |
Type of publication: | Book / Working Paper |
Notes: | Number Ifo Working Paper No. 171 |
Classification: | C32 - Time-Series Models ; C52 - Model Evaluation and Testing ; C53 - Forecasting and Other Model Applications ; E37 - Forecasting and Simulation ; R11 - Regional Economic Activity: Growth, Development, and Changes |
Source: |
Persistent link: https://www.econbiz.de/10010877592
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