Forecasting with Large Unbalanced Datasets: The Mixed-Frequency Three-Pass Regression Filter
Dr. Christian Hepenstrick and Massimiliano Marcellino
E37, C32, C53
Dynamic Factor Models, Mixed Frequency, GDP Nowcasting, Forecasting, Partial Least Squares
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.