Median-based seasonal adjustment in the presence of seasonal volatility

Abstract

Philippine seasonal time series data tends to have unstable seasonal behavior, called seasonal volatility. Current Philippine seasonal adjustment methods use X-11-ARIMA, which has been shown to be poor in the presence of seasonal volatility. A modification of the Census X-11 method for seasonal adjustment is devised by changing the moving average filters into median-based filtering procedures using Tukey repeated median smoothing techniques. To study the ability of the new procedure, simulation experiments and application to real Philippine time series data were conducted and compared to Census X-11-ARIMA methods. The seasonal adjustment results will be evaluated based on their revision history, smoothness and accuracy in estimating the non-seasonal component. The results of research open the idea of using robust nonlinear filtering methods as an alternative in seasonal adjustment when moving average filters tend to fail under unfavorable conditions of time series data