Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/233666 
Year of Publication: 
2021
Citation: 
[Journal:] Journal of Forecasting [ISSN:] 1099-131X [Volume:] 40 [Issue:] 5 [Publisher:] Wiley [Place:] Hoboken, NJ [Year:] 2021 [Pages:] 861-882
Publisher: 
Wiley, Hoboken, NJ
Abstract: 
Germany's economic composition is heterogenous across regions, which makes regional economic projections based on German gross domestic product (GDP) growth unreliable. In this paper, we develop forecasting models for Baden-Württemberg's economic growth, a regional economy that is dominated by small- and medium-sized enterprises with a strong focus on foreign trade. For this purpose, we evaluate the backcasting and nowcasting performance of mixed data sampling (MIDAS) regressions with forecast combinations against an approximate dynamic mixed-frequency factor model. Considering a wide range of regional, national, and global predictors, we find that our high-dimensional models outperform benchmark time series models. Surprisingly, we also find that combined forecasts based on simple single-predictor MIDAS regressions are able to outperform forecasts from more sophisticated dynamic factor models.
Subjects: 
backcasting
Germany
high dimensional
nowcasting
regional
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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