Taming volatile high frequency data with long lag structure: An optimal filtering approach for forecasting

Dirk Drechsel and Stefan Neuwirth ()
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Stefan Neuwirth: KOF Swiss Economic Institute, ETH Zurich, Switzerland

No 16-407, KOF Working papers from KOF Swiss Economic Institute, ETH 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.

Pages: 26 pages
Date: 2016-01
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-net
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