textabstractEach 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.

中文翻译：

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纠正一月乐观效应

textabstract每个月，各种专业预测员都会对明年的实际国内生产总值（GDP）增长和失业率进行预测。一月是一个特殊的月份，当预测范围移至下一个日历年时。在分析预测更新时，我没有删除 1 月份的数据，而是提出了一个定期版本的测试回归以提高弱形式效率。将该周期模型应用于一系列国家的许多预测表明，1 月份 GDP 预测更新为正，而失业率预测更新为负。我记录了今年 1 月对新日历年的乐观态度不利于预测的准确性。为了实证分析奥肯定律，我还提出了一个周期检验回归，它的应用为这个定律提供了更多的支持。