Nowcasting German GDP: Foreign factors, financial markets, and model averaging

B-Tier
Journal: International Journal of Forecasting
Year: 2023
Volume: 39
Issue: 1
Pages: 298-313

Authors (5)

Andreini, Paolo (not in RePEc) Hasenzagl, Thomas (not in RePEc) Reichlin, Lucrezia (London Business School (LBS)) Senftleben-König, Charlotte (not in RePEc) Strohsal, Till (Hochschule für Wirtschaft und ...)

Score contribution per author:

0.402 = (α=2.01 / 5 authors) × 1.0x B-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

Abstract

This paper develops a nowcasting model for the German economy. The model outperforms a number of alternatives and produces forecasts not only for GDP but also for other key variables. We show that the inclusion of a foreign factor improves the model’s performance, while financial variables do not. Additionally, a comprehensive model averaging exercise reveals that factor extraction in a single model delivers slightly better results than averaging across models. Finally, we estimate a “news” index for the German economy in order to assess the overall performance of the model beyond forecast errors in GDP. The index is constructed as a weighted average of the nowcast errors related to each variable included in the model.

Technical Details

RePEc Handle
repec:eee:intfor:v:39:y:2023:i:1:p:298-313
Journal Field
Econometrics
Author Count
5
Added to Database
2026-01-29