🤖 AI Summary
This study addresses the uncertainty inherent in seasonal adjustment arising from the unobservability of seasonal components in economic analysis. We propose a probabilistic model discovery method based on Bayesian inference that decomposes time series into seasonal and non-seasonal components, yielding posterior distributions over both structures and parameters. The core innovation lies in establishing the first probabilistic framework to quantify real-time uncertainty in seasonal adjustment, thereby overcoming the limitations of conventional deterministic approaches. Experiments on simulated and macroeconomic datasets demonstrate that the proposed method significantly improves both point and interval forecasting accuracy. Furthermore, it enables the early detection of adjustment risks that traditional tools such as X-13 fail to capture.
📝 Abstract
Seasonal adjustment is fundamental to economic analysis, but uncertain because seasonal components are inherently latent. This article introduces a probabilistic model discovery method that decomposes a time series into seasonal and nonseasonal components. The method returns a posterior distribution over the structure and parameters of a seasonal component. In simulation studies, the method can improve point forecasts, interval predictions, and recovery of seasonal components relative to X-13ARIMA-SEATS. In a study of eight U.S. macroeconomic series during the COVID-19 recession, the method surfaces significant ex-ante uncertainty about current seasonal adjustments in real time, well before many X-13 revisions reach their eventual peaks.