Forecasting with Large Unbalanced Datasets: The Mixed-Frequency Three-Pass Regression Filter
Christian Hepenstrick () and
No 2016-04, Working Papers from Swiss National Bank
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
References: Add references at CitEc
Citations: View citations in EconPapers (4) Track citations by RSS feed
Downloads: (external link)
https://www.snb.ch/n/mmr/reference/working_paper_2 ... _paper_2016_04.n.pdf (application/pdf)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:snb:snbwpa:2016-04
Access Statistics for this paper
More papers in Working Papers from Swiss National Bank Contact information at EDIRC.
Bibliographic data for series maintained by Enzo Rossi ().