Mixed-frequency models for tracking short-term economic developments in Switzerland
Alain Galli (),
Christian Hepenstrick () and
Rolf Scheufele ()
No 2017-02, Working Papers from Swiss National Bank
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.
Keywords: Mixed frequency; GDP; nowcasting; forecasting; Switzerland (search for similar items in EconPapers)
JEL-codes: C32 C53 E37 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-fdg, nep-for and nep-mac
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Journal Article: Mixed-Frequency Models for Tracking Short-Term Economic Developments in Switzerland (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:snb:snbwpa:2017-02
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