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Using low frequency information for predicting high frequency variables

Author

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  • Foroni, Claudia
  • Guérin, Pierre
  • Marcellino, Massimiliano
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.

Suggested Citation

  • Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2018. "Using low frequency information for predicting high frequency variables," International Journal of Forecasting, Elsevier, vol. 34(4), pages 774-787.
    • Handle: RePEc:eee:intfor:v:34:y:2018:i:4:p:774-787
    DOI: 10.1016/j.ijforecast.2018.06.004
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    More about this item

    Keywords

    Mixed-frequency VAR models; Temporal aggregation; MIDAS models;
    All these keywords.

    JEL classification:

    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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