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Robust Real‐Time Estimates of the German Output Gap Based on a Multivariate Trend‐Cycle Decomposition

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  • Tino Berger
  • Christian Ochsner

Abstract

The German economy is an important economic driver in the Euro area in terms of gross domestic product, labor force, and international integration. We provide a state of the art estimate of the German output gap between 1995 and 2022 and present a nowcasting scheme that accurately predicts the German output gap up to 3 months prior to a gross domestic product data release. To this end, we elicit a mixed‐frequency Bayesian vector‐autoregressive model (MF‐BVAR) using monthly information to form an expectations about the current‐quarter output gap. The mean absolute error of the MF‐BVAR nowcast compared to the final estimate is very small (0.28 percentage points) after only 1 month of observed data. Moreover, we show that business and consumer expectations, international trade, and labor market aggregates consistently explain large shares of variation in the German output gap. Finally, the MF‐BVAR procedure is very reliable, as it implies an output gap that is hardly revised ex post. This is particularly important for policymakers.

Suggested Citation

  • Tino Berger & Christian Ochsner, 2026. "Robust Real‐Time Estimates of the German Output Gap Based on a Multivariate Trend‐Cycle Decomposition," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(3), pages 1129-1144, April.
    • Handle: RePEc:wly:jforec:v:45:y:2026:i:3:p:1129-1144
    DOI: 10.1002/for.70079
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