Many economic data series exhibit strong seasonal effects. For instance, home sales activity generally picks up in the spring and summer, and is slower in the fall and winter. These seasonal patterns can make it hard to track underlying trends in the data. For example, if home sales decline in September, is that just a normal seasonal pattern, or does it indicate the market has weakened?
Seasonal adjustment techniques are used to help “see through” normal seasonal patterns to identify underlying trends. A variety of methods are used to seasonally adjust data, but they all involve decreasing the values to offset normal seasonal highs and increasing them to offset seasonal lows.
