Bayesian Seasonal Adjustment of Long Memory Time Series

Existing approaches to the seasonal adjustment of economic time series are typically either nonparametric or model-based. In both cases, the goal is to remove seasonal variation from the time series. In each of the two paradigms, both the seasonally adjusted series and the seasonal component are latent processes. As such, seasonal adjustment can be viewed as an unobserved components (UC) problem and specifically that of UC estimation. Though the nonparametric approach has a rich history going back to the development of X-11 and X-11 ARIMA (Dagum 1980; Shiskin et al. 1967), our focus centers on model-based methodology.