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Forecasting with Large Unbalanced Datasets: The Mixed-Frequency Three-Pass Regression Filter

Christian Hepenstrick () and Massimiliano Marcellino

No 2016-04, Working Papers from Swiss National Bank

Abstract: In this paper, we propose a modification of the three-pass regression filter (3PRF) to make it applicable to large mixed frequency datasets with ragged edges in a forecasting context. The resulting method, labeled MF-3PRF, is very simple but compares well to alternative mixed frequency factor estimation procedures in terms of theoretical properties, finite samle performance in Monte Carlo experiments, and empirical applications to GDP growth nowcasting and forecasting for the USA and a variety of other countries.

Keywords: Dynamic Factor Models; Mixed Frequency; GDP Nowcasting; Forecasting; Partial Least Squares (search for similar items in EconPapers)
JEL-codes: E37 C32 C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-for and nep-mac
Date: 2016
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