Using Low Frequency Information for Predicting High Frequency Variables

Arbeitspapier

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.

Object Details

• ISBN: 978-82-7553-878-7
• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Paper ; No. 13/2015

Classification and Topics

• Classification: Wirtschaft ; Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications ; Forecasting Models; Simulation Methods
• Subject: MIDAS model ; mixed frequency VAR models ; temporal aggregation

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Foroni, Claudia ; Guérin, Pierre ; Marcellino, Massimiliano
• Event: Veröffentlichung
• (who): Norges Bank
• (where): Oslo
• (when): 2015

Further information

• Handle: http://hdl.handle.net/10419/210080
• Last update: 10.03.2025, 11:42 AM CET