There are two different ways to deal with seasonality in economic data. One approach is to try to model it explicitly. We might, for example, attempt to explain the seasonal variation in a dependent variable by the seasonal variation in some of the independent variables, perhaps including weather variables or, more commonly, seasonal dummy variables, which were discussed in Section 2.5. Alternatively, we can model the error terms as following a seasonal ARMA process, or we can explicitly estimate a seasonal ADL model. The second way to deal with seasonality is usually less satisfactory. It depends on the use of seasonally adjusted data, that is, data which have been massaged in such a way that they represent what the series would supposedly have been in the absence of seasonal variation. Indeed, many statistical agencies release only seasonally adjusted data for many time series, and economists often treat these data as if they were genuine. However, as we will see later in this section, using seasonally adjusted data can have unfortunate consequences.