Why bother with seasonal adjustment?

Why bother with seasonal adjustment, extreme adjustment, and smoothing?  Because often the unprocessed data are not very clear.

The chart below shows the original (unadjusted), seasonally and extreme-adjusted, and the smoothed data for Turkey's total vehicle sales.

Notice the extreme seasonality.   

If you just looked at the original data, you might conclude from December 2023's (seasonal) spike that vehicle sales were doing very well.   But in fact, on a seasonally adjusted basis, vehicle sales are down from earlier this year, which is consistent with the sharp monetary tightening by the Bank of Türkiye over the last six months.   In fact, the BOT started raising interest rates in July, which is also when the seasonally adjusted sales peaked.

The smoothed version of the seasonally adjusted data has turned down, suggesting that this is a downturn which will last more than a couple of months.  The smoothed data peaked in July at 105,000 units and have fallen steadily since to 103,000 units in December.

 

Click to see clearer image

Here's what the data look like over a shorter time period:

Click to see clearer image

I should point out that the seasonal and extreme adjustment are done by programs I've written, based on algorithms created by the US Bureau of the Census.  I make no manual adjustments; it's all automated.  However, I can choose the length and type of the smoothing.  In this case, I used a 3-month centred linear moving average of a 9-month centred linear moving average with weights calculated by the Bureau of the Census for the X-11 seasonal adjustment program.  This is called a 3 x 9 moving average.  "Linear" refers to the pattern of weights, which is the same for each month in the central span of the time series.  You can also use quadratic or quartic moving averages.  The moving averages are "centred", because a conventional moving average lags the turning points in the underlying data. If you use different moving averages for different time series, because they have different "spikiness", you would get spurious variations in cyclical turning points between the series, not derived from the underlying data, but a function of the length of the moving average you've chosen. 

Quite technical stuff, but I thought you might be interested.