Template-type: ReDIF-Paper 1.0
Author-Name: Dr. Christian Hepenstrick
Author-Name-First: Christian
Author-Name-Last: Hepenstrick
Author-Person: phe328
Author-Name: Massimiliano Marcellino
Author-Name-First: Massimiliano
Author-Name-Last: Marcellino
Title: Forecasting with Large Unbalanced Datasets: The Mixed-Frequency Three-Pass Regression Filter
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.
Length: 44 pages
Creation-Date: 2016
Contact-Email: forschung@snb.ch
File-URL: https://www.snb.ch/en/publications/research/working-papers/2016/working_paper_2016_04
File-Format: text/html
Number: 2016-04
Classification-JEL: E37, C32, C53
Keywords: Dynamic Factor Models, Mixed Frequency, GDP Nowcasting, Forecasting, Partial Least Squares
Handle: RePEc:snb:snbwpa:2016-04