Mixed-frequency models for tracking short-term economic developments in Switzerland
Dr. Alain Galli, Dr. Christian Hepenstrick and Dr. Rolf Scheufele
C32, C53, E37
Mixed frequency, GDP, nowcasting, forecasting, Switzerland
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.