ABSTRACT
Over the years this area had made significant progress, from both theoretical and programmatic aspects. On a parallel track, the issue of decomposition of time series, and in particular the development of seasonal adjustment software algorithms, has provided a great service to users of empirical data. A number of these watershed approaches are presented and discussed in this monograph, such as the work of Beveridge and Nelson, and Nelson and Plosser. In this monograph, an attempt is made to understand these pre-adjustment methods and to demarcate and utilize them in empirical analyses in a number of case studies. It was shown on a number of empirical cases that automatic procedures depends upon built-in options chosen within the program. Also, Case Study 2 examined the accuracy of forecasting models by utilizing seasonally adjusted data against non-seasonally adjusted data.
