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.

中文翻译：

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预测巴登-符腾堡州的 GDP 增长：MIDAS 回归与动态混合频率因子模型

德国的经济构成因地区而异，这使得基于德国国内生产总值 (GDP) 增长的地区经济预测不可靠。在本文中，我们开发了巴登-符腾堡州经济增长的预测模型，该地区经济以中小企业为主，重点关注外贸。为此，我们根据近似动态混合频率因子模型评估混合数据采样 (MIDAS) 回归与预测组合的反向预测和临近预测性能。考虑到范围广泛的区域、国家和全球预测因素，我们发现我们的高维模型优于基准时间序列模型。出奇，