ABSTRACT
This chapter talks about one of the attractive features of the Box-Jenkins (BJ) approach which is a very rich class of possible models, and it is usually possible to find one that can provide an adequate description of the time series process. The first step in BJ approach is to determine if the time series is stationary. BJ recommend differencing non-stationary series one or more times to achieve stationarity. In general, seasonal adjustment refers to a smoothing procedure, that is, it involves the process of estimating and removing the seasonal effects from a time series. In general, there are two kinds of seasonal effects in time series data. The first is known as the deterministic seasonal effect. The second seasonal effect is known as the probabilistic one. The main interest of economists is to find a solution that incorporates these seasonal phenomena, so that future seasonal effects could be reflected in the forecasting procedure.
