Taming volatile high frequency data with long lag structure: An optimal filtering approach for forecasting - Dirk Drechsel; Stefan Neuwirth (ETH Zurich, KOF Swiss Economic Institute, 2016)
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.
Keywords: 330; Forecasting; construction; Switzerland; Bayesian; mixed data frequencies