A survey of recent developments in seasonal adjustment

Abstract In recent years there have been notable advances in the methodology for analyzing seasonal time series. This paper summarizes some recent research on seasonal adjustment problems and procedures. Included are signal-extraction methods based on autoregressive integrated moving average (ARIMA) models, improvements in X–11, revisions in preliminary seasonal factors, regression and other model-based methods, robust methods, seasonal model identification, aggregation, interrelating seasonally adjusted series, and causal approaches to seasonal adjustment.