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Using low frequency information for predicting high frequency variables

Claudia Foroni, Pierre Guérin and Massimiliano Marcellino

No 2015/13, Working Paper from Norges Bank

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.

Keywords: Mixed-Frequency VAR models; temporal aggregation; MIDAS models (search for similar items in EconPapers)
JEL-codes: C53 E37 (search for similar items in EconPapers)
Pages: 41 pages
Date: 2015-10-29
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for, nep-mac, nep-mst and nep-ore
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Journal Article: Using low frequency information for predicting high frequency variables (2018) Downloads
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