Abstract
AbstractEach month, various professional forecasters give forecasts for next year's real gross domestic product (GDP) growth and unemployment. January is a special month, when the forecast horizon moves to the following calendar year. Instead of deleting the January data when analyzing forecast updates, I propose a periodic version of a test regression for weak‐form efficiency. An application of this periodic model for many forecasts across a range of countries shows that in January GDP forecast updates are positive, whereas the forecast updates for unemployment are negative. I document that this January optimism about the new calendar year is detrimental to forecast accuracy. To empirically analyze Okun's law, I also propose a periodic test regression, and its application provides more support for this law.
Highlights
Professional forecasters, like those in the Survey of Professional Forecasters1 and the Consensus Forecasters,2 can quote forecasts in each month of the year
I examine the forecasts created by professional forecasters to see whether a January effect exists for their forecasts when testing for weakform efficiency and Okun's law
An application of a periodic model for weakform efficiency of forecast updates across a range of countries showed that, in January, gross domestic product (GDP) forecast updates are positive and the forecast updates for unemployment are negative
Summary
Professional forecasters, like those in the Survey of Professional Forecasters1 and the Consensus Forecasters,2 can quote forecasts in each month of the year. KEYWORDS forecast updates, January effect, Okun's law, periodic regression model, weak-form efficiency
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