Major financial and economic changes of global character have introduced high levels of variability in time series data, particularly, in_ socioeconomic indicators often used for the analysis of current economic conditions, referred to as recession and recovery analysis. Traditionally, these types of indicators were seasonally adjusted to determine the direction of the short-term trend for an early detection of a turning point. However, their current high level of variability has created the need for further smoothing in order to suppress part of the noise without affecting the underlying trend.In a recent study, Dagum (1996) developed a new method to improve the classical 13-term Henderson linear filter, which is the most applied for short-term trendcycle estimation by statistical agencies. This new method has the advantage of reducing the number of unwanted ripples (very short cycles that can be falsely inter-