What Is a Seasonal Adjustment?

A seasonal adjustment is a statistical technique designed to even out periodic swings in statistics or movements in supply and demand related to changing seasons. It can, therefore, eliminate misleading seasonal components of an economic time series. Seasonal adjustment is a method of data-smoothing that is used to predict economic performance or company sales for a given period.

Seasonal adjustments provide a clearer view of nonseasonal trends and cyclical data that would otherwise be overshadowed by the seasonal differences. This adjustment allows economists and statisticians to better understand the underlying, base trends in a given time series.

An annual rate that has been adjusted to account for seasonal fluctuations in data are therefore known as the seasonally adjusted annual rate (SAAR). To calculate the SAAR for a given year, divide the unadjusted rate for a given month by its seasonality factor, and then multiply that figure by 12 to extrapolate an annual rate. If quarterly data are being used instead, multiply by four.

Seasonal Adjustment Explained

Seasonal adjustments are intended to smooth out aberrations in certain types of financial activity. For example, the U.S. Bureau of Labor Statistics (BLS) uses seasonal adjustment to achieve a more accurate portrait of employment and unemployment levels in the United States. They do this by removing the influence of seasonal events, such as the holidays, weather events, school schedules, and even the harvest period.

Seasonal events are temporary, usually have a known length, and they tend to follow a generally predictable pattern each year, at the same time of year. As a result, seasonal adjustments can remove their influence on statistical trends. Adjustments allow statisticians to more easily observe nonseasonal and underlying trends and cycles and get an accurate and useful view of the labor market and buying habits.

These adjustments are estimates based on seasonal activity in previous years.

Key Takeaways

  • Seasonal adjustments provide a clearer view of nonseasonal changes in data.
  • Adjustments are used to smooth out aberrations in certain types of financial activity.
  • The estimated are based on the effects of the previous year's fixed event.

Seasonal Adjustments Expose Underlying Trends

Seasonal movements can be substantial, so much so that they can often obscure other traits and trends in the data. If seasonal adjustments are not made, analyses of the data cannot yield accurate results. If each period in a time series—for example, each month in the fiscal year—has a different tendency toward low or high seasonal values, it can be difficult to detect the true direction of the underlying trends of the time series. Difficulties include increases or decreases in economic activity, turning points, and other economic indicators.

How the Consumer Price Index Uses Seasonal Adjustment

The consumer price index (CPI) uses X-13ARIMA-SEATS seasonal adjustment software to perform seasonal adjustments of pricing data that is deemed subject to seasonal adjustments such as motor fuels, food and beverage items, vehicles, and some utilities.

CPI economists re-evaluate the seasonal status of each data series each year. To do this, they calculate new seasonal factors each January and apply them to the last five years of index data. Indexes older than five-years-old are considered final and are no longer revised. The Bureau of Labor Statistics reevaluates whether each series should remain seasonally adjusted or not, based upon specific statistical criteria. Intervention analysis seasonal adjustment is used when a single, nonseasonal event influences seasonally-adjusted data.

For example, when the global recession in 2008 affected fuel prices, intervention analysis seasonal adjustment was used to offset its effects on fuel pricing in that year. Using these methods, the CPI can formulate more accurate price indexes for components and indexes that aren't subject to seasonal adjustment.

Real World Example of a Seasonal Adjustment

For example, the sales of running shoes bought in the summer exceed the amount bought in the winter. This increase is due to the seasonal factor that more people run, or participate in other outdoor activities requiring similar footwear, in the summer.

The seasonal spike in running shoe sales can obscure the general trends in athletic footwear sales across the whole time series. A seasonal adjustment is therefore made to obtain a clear picture of the general trend.