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.
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.