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Forecasting Baden‐Württemberg's GDP growth: MIDAS regressions versus dynamic mixed‐frequency factor models

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  • Konstantin Kuck
  • Karsten Schweikert

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.

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  • Konstantin Kuck & Karsten Schweikert, 2021. "Forecasting Baden‐Württemberg's GDP growth: MIDAS regressions versus dynamic mixed‐frequency factor models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 861-882, August.
    • Handle: RePEc:wly:jforec:v:40:y:2021:i:5:p:861-882
    DOI: 10.1002/for.2743
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    6. Hwee Kwan Chow & Yijie Fei & Daniel Han, 2023. "Forecasting GDP with many predictors in a small open economy: forecast or information pooling?," Empirical Economics, Springer, vol. 65(2), pages 805-829, August.

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