Mixed-frequency models for tracking short-term economic developments in Switzerland

February 1, 2017
Issue 2017-02

Summary

We compare several methods for monitoring short-term economic developments in Switzerland. Based on a large mixed-frequency data set, the following approaches are presented and discussed: factor-based information combination approaches (including factor model versions based on the Kalman filter/smoother, a principal component based version and the three-pass regression filter), a model combination approach resting on MIDAS regression models and a model selection approach using a specific-to-general algorithm. In an out-of-sample GDP forecasting exercise, we show that the considered approaches clearly beat relevant benchmarks such as univariate time-series models and models that work with one or a small number of indicators. This suggests that a large data set is an important ingredient for successful real-time monitoring of the Swiss economy. The models using a large data set particularly outperform others during and after the Great Recession. Forecast pooling of the most-promising methods turns out to be the best option for obtaining a reliable nowcast for the Swiss economy.

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Issue:
02
Pages:
42
JEL classification:
C32, C53, E37
Keywords:
Mixed frequency, GDP, nowcasting, forecasting, Switzerland
Year:
2017

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Author(s)

  • Dr. Alain Galli

  • Dr. Christian Hepenstrick

  • Dr. Rolf Scheufele

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