Each month various professional forecasters give forecasts for next year's real GDP growth and many other variables. In terms of forecast updates, January is a special month, as then the forecast horizon moves to the following calendar year, and as such the observation is not a revision. Instead of deleting the January data when analyzing forecast updates, this paper proposes a periodic version of an often considered test regression, to explicitly include and model the January data. An application of this periodic model for many forecasts across a range of countries learns that apparently there is a January optimism effect. In fact, in January, GDP forecast updates are suddenly positive, and at the same time the forecast updates for unemployment are likewise negative. This optimism about the new year of the professional forecasters is however found to be detrimental to forecast accuracy. The main conclusion is that forecasts created in January for the next year need to be treated with care.

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Department of Econometrics

Franses, P. H. (2019). Professional Forecasters and January. Retrieved from http://hdl.handle.net/1765/118711