Economics at your fingertips  

Using low frequency information for predicting high frequency variables

Claudia Foroni, Pierre Guérin () and Massimiliano Marcellino

International Journal of Forecasting, 2018, vol. 34, issue 4, 774-787

Abstract: We analyze ways of incorporating low frequency information into models for the prediction of high frequency variables. In doing so, we consider the two existing versions of the mixed frequency VAR, with a focus on the forecasts for the high frequency variables. Furthermore, we introduce new models, namely the reverse unrestricted MIDAS (RU-MIDAS) and reverse MIDAS (R-MIDAS), which can be used for producing forecasts of high frequency variables that also incorporate low frequency information. We then conduct several empirical applications for assessing the relevance of quarterly survey data for forecasting a set of monthly macroeconomic indicators. Overall, it turns out that low frequency information is important, particularly when it has just been released.

Keywords: Mixed-frequency VAR models; Temporal aggregation; MIDAS models (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5) Track citations by RSS feed

Downloads: (external link)
Full text for ScienceDirect subscribers only

Related works:
Working Paper: Using low frequency information for predicting high frequency variables (2015) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

DOI: 10.1016/j.ijforecast.2018.06.004

Access Statistics for this article

International Journal of Forecasting is currently edited by R. J. Hyndman

More articles in International Journal of Forecasting from Elsevier
Bibliographic data for series maintained by Haili He ().

Page updated 2020-06-29
Handle: RePEc:eee:intfor:v:34:y:2018:i:4:p:774-787