Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/241205 
Year of Publication: 
2020
Series/Report no.: 
Bank of Canada Staff Working Paper No. 2020-39
Publisher: 
Bank of Canada, Ottawa
Abstract: 
We propose a new no-change benchmark to evaluate forecasts of series that are temporally aggregated. The new benchmark is the last high-frequency observation and reflects the null hypothesis that the underlying series, rather than the aggregated series, is unpredictable. Under the random walk null hypothesis, using the last high-frequency observation improves the mean squared prediction errors of the no-change forecast constructed from average monthly or quarterly data by up to 45 percent. We apply this insight to forecasts of the real price of crude oil and show that a new benchmark that relies on monthly closing prices dominates the conventional no-change forecast in terms of forecast accuracy. Although model-based forecasts also improve when models are estimated using closing prices, only the futures-based forecast significantly outperforms the new benchmark. Introducing a more suitable benchmark changes the assessments of different forecasting approaches and of the general predictability of real oil prices.
Subjects: 
Econometric and statistical methods
International topics
JEL: 
C1
C53
Q47
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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