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Understanding the Importance of Seasonality in Economics

John Wickham and Thomas Bolland

Not Seasonally Adjusted

When analyzing economic data it is imperative that one takes into consideration the seasonal nature of economics. Seasonality is the characteristics of a time series that exhibits predictability and regular occurrence on a yearly basis. Seasonality incorporates major holidays, school scheduling, weather, and agricultural changes among other factors. By taking  these factors into consideration and adjusting the graph to remove seasonal bias, a much clearer picture emerges about growth.

Seasonally Adjusted

This is imperative to understand the change in the value of US Imports. The two charts displayed show the growth of the value of US imports between 2010 and 2017. The first chart, which is not quarterly seasonally adjusted, displays osciliations between quarters. The trend of the unadjusted graph shows that Quarter 1 is the lowest in value, Quarter 3 is the highest in value with minimal growth during Quarters 2 and 4. The reason for this is the Financial reporting factor because industries attempt to increase sales to make their respective financial reportings look better.  When viewing the seasonally adjusted graph, it is much easier to show the upward trend of the value of United States imports over the last seven years. Following the recession of 2008, there was a significant spike in the value of value of imports, stopping in 2012. Since 2012, minus a slight fluctuation between 2014 and 2016, the value of goods imported have plateaued. The chart suggests that this will be the trend for the near future as the value of imported goods has steadied out.

Comparing the two graphs, the importance of seasonal adjustment become clear. Without seasonal adjustment, the overall long-run growth trend would be muted by the seasonal effects on the value of imports. In addition season adjustment allows comparison between different graphs over various periods of time. However, there is a downside to seasonal adjustment. There is no precise way to determine seasonal adjustment since it is based off hypothesis data. In addition to that, if the seasonal adjustment is incorrect, then the data becomes flawed and will lead to a false conclusion. However, on the whole, seasonal adjustment is need to determine long-term growth trends.

13 thoughts on “Understanding the Importance of Seasonality in Economics

  1. clintong20

    This is extremely helpful! When creating my blog post I found myself trying to decode the meaning of seasonality within graphs. It simply makes it clear cut and even more accurate. Thank you for clearing this up!

  2. Katie Paton

    Looking at the seasonal effects of imports is very interesting, especially during the holidays. The level of imports around Black Friday and Christmas will be much higher than right after the holidays, so graphs should be adjusted to ensure that the reader gets a clear interpretations of the level of imports. I wonder how economics determined the most accurate way to seasonally adjust data?

  3. Lauren Fredericks

    This post helpfully and effectively displays the value of taking seasonality into consideration! The graph adjusted for seasonality really does reveal the true nature of the data. However, are we sure that the value of US imports has plateaued, or did it simply dip in 2016 and now it is slowly making it's way up again? (I don't actually know, I'm just curious)

  4. spencerc20

    This post was very helpful and I wish we had taken some amount of seasonal adjustment into account when we were doing our post on the recessions and unemployment. It would have been interesting to see how different holiday seasons or agricultural seasons affect the rate of unemployment. I think this will lead to a lot of interesting new posts or edits on existing posts in order to get data that's less volatile.

  5. johnsonjm20

    This a very informative post. like many above, I didn't fully understand what seasonal adjustment was. I think this post does a good job of explaining that. It does make wonder, when looking at data, is it best to look at seasonally adjusted date or unadjusted data? I know each one has its positives and negatives but which is better to use.

  6. trammellc20

    I found this post very helpful, as many other commenters have said, because I didn't know the real meaning of seasonality and the big impact it can have. I found it especially interesting because I thought the non-adjusted graphs were misleading because they didn't convey all the information about the subject. I thought that the non-seasonally adjusted graphs were somewhat dangerous if they are being presented as the full story of data to the public.

  7. warej20

    This post provided a great clarification of the differences between seasonally adjusted data and non-seasonally adjusted data. The ability to seasonally adjust a graph seems like a great tool for understanding and displaying long term trends. In the future when I am displaying graphs I will be sure to keep this in mind so that I can effectively portray my argument in graphical form.

  8. bearupk20

    This post gave some great insight into why seasonality matters and how it can skew data in graphs and let the viewer/reader perceive something which is not necessarily true. It is important to know how seasons affect data and why this information should be kept in mind when reading graphs and charts that are not seasonally adjusted.

  9. longa20

    Very interesting examination of seasonality, as it is something that I have never thought about. It is evident that it smooths out graphs and supports or denotes growth. Do you think it would be possible for the opposite to occur and seasonality to detract from what actually occurs?

    1. alisonw20

      Aidan, because seasonality is a hypothesis it is likely to have flaws and produce values that are wrong. This means that a seasonality graph would predict an inaccurate long run trend. However, seasonal graphs are definitely useful to get an idea of what trends might do in the future.

  10. the prof

    Good to see careful thinking about data issues. Time series data can be really hard to disentangle, real vs nominal, seasonal vs adjusted, and (not something we discuss) trends vs random walks, and growth rates of a variable vs levels of a variable.

  11. myerse20

    It's crazy how much of a difference seasonality makes. You chose great graphs to display that! It's very interesting to me that it is the financial reporting factor that accounts for so much of the seasonal fluctuations, but the way you explained it makes it make perfect sense. Great insight!

  12. hallk20

    It will be really interesting to see how the internet plays a role in these seasonal spending habits. As people have access right at their fingertips to anything they could need, perhaps seasonal spending will even out. I am thinking about 'holidays' like black friday, which has greatly suffered due to the internet.

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