국제금융
Selecting seasonal filters in X-13-ARIMA via cross-validation
How can we improve the accuracy of seasonal adjustments in economic data? Official statistics often rely on heuristic rules to select seasonal filters, but are these methods optimal? This study explores the use of cross-validation, a data-driven technique widely used in machine learning, to enhance seasonal filter selection in the X-13-ARIMA method. The findings suggest that cross-validation not only matches but often outperforms traditional methods. Given its flexibility with respect to adding new filters, it is also a promising tool in the seasonal adjustment of high-frequency data.
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