Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/148971 
Year of Publication: 
2016
Series/Report no.: 
KOF Working Papers No. 407
Publisher: 
ETH Zurich, KOF Swiss Economic Institute, Zurich
Abstract: 
We propose a Bayesian optimal filtering setup for improving out-of-sample forecasting performance when using volatile high frequency data with long lag structure for forecasting low-frequency data. We test this setup by using real-time Swiss construction investment and construction permit data. We compare our approach to different filtering techniques and show that our proposed filter outperforms various commonly used filtering techniques in terms of extracting the more relevant signal of the indicator series for forecasting.
Subjects: 
Forecasting
construction
Switzerland
Bayesian
mixed data frequencies
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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