As undergraduates, we are taught to make sure the macroeconomic data we are dealing with is seasonally adjusted. We are explained that statistical offices remove the seasonal factors is a way that is close to regressing the data on seasonal dummies and taking moving averages. If you really look into this, as so often, it turns out things are much more complex than that, and subtleties matter.
Stephen Pollock and Emi Mise do a technical review of the various methods and look at some alternatives. Broadly speaking, there are three strands of techniques. The first is based on ARIMA, the second removes seasonal frequencies found in a periodogram, and the third relies on clear distinctions between fundamental and seasonal components in spectral analysis. The difficulties are compounded by the fact that data usually has a trend, which may not be loglinear, and data thus requires pre- and post-treating. And as Pollock and Mise show, each of these methods matter, even for dating turning points. I can imagine this can become even more important when one throws data into a regression, especially if the series have been detrended in different ways. And it is rare to see statistical offices declare what method was used for that.