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

Dr. Christian Hepenstrick and Massimiliano Marcellino

Issue
2016-04

Pages
44

JEL classification
E37, C32, C53

Keywords
Dynamic Factor Models, Mixed Frequency, GDP Nowcasting, Forecasting, Partial Least Squares

Year
2016

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.