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Using low frequency information for predicting high frequency variables
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DOI：10.1016/j.ijforecast.2018.06.004.png)
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Abstract
En 中文
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. (C) 2018 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
Keywords:
Mixed-frequency VAR models
Temporal aggregation
MIDAS models
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