Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/210080 
Year of Publication: 
2015
Series/Report no.: 
Working Paper No. 13/2015
Publisher: 
Norges Bank, Oslo
Abstract: 
We analyze how to incorporate low frequency information in models for predicting high frequency variables. In doing so, we introduce a new model, the reverse unrestricted MIDAS (RU-MIDAS), which has a periodic structure but can be estimated by simple least squares methods and used to produce forecasts of high frequency variables that also incorporate low frequency information. We compare this model with two versions of the mixed frequency VAR, which so far had been only applied to study the reverse problem, that is, using the high frequency information for predicting low frequency variables. We then implement a simulation study to evaluate the relative forecasting ability of the alternative models in finite samples. Finally, we conduct several empirical applications to assess 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 so when it is just released.
Subjects: 
MIDAS model
mixed frequency VAR models
temporal aggregation
JEL: 
E37
C53
Persistent Identifier of the first edition: 
ISBN: 
978-82-7553-878-7
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Working Paper
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