I normally post articles that are useful to read. The author of the following article does themselves no favours with a general rant against official statistics outputs. I’ll quote two parts of the article that deserve to be highlighted:
The full article is available here: http://www.cityam.com/220794/retail-sales-paint-the-wrong-picture
With the first quote of:
“… comprehensive compilation of data is useful for economists and analysts, it is broadly unhelpful for anyone who wants to get a simple understanding of the direction of retail.”
I’d argue that nothing is simple anymore when it comes to interpreting movements in economic data. Simple analysis will lead to simple understanding and when it comes to the complex nature of economic outputs and estimates you are going to miss the subtle underlying aspects of the economic picture. Why constrain your analysis to simple measures and one indicator? The best approach is to build a picture of a set of outputs and look at a range of indicators. Seasonally adjusted and trend estimates can help give a useful picture when used in tandem; including even looking at the unadjusted estimates if needed. Just focusing on a single indicator is a recipe for disaster for interpretation and understanding as each indicator has its own strength and weakness.
The second quote from the article backs this up. The quote is:
“The more sensible measure is to look at the value of spend compared to the same period last year.”
Unfortunately this is a common misconception but ends up resulting in flawed analysis. It shows a lack of understanding of time series analysis issues in general. Firstly, depending on what data is being compared, year-on-year movements in non-seasonally adjusted estimates run the risk of resulting in seriously misleading analysis. This is because the nature of the calendar changes over time. July this year in 2015 has a different number of day composition compared to July last year. July 2015 has 4 weeks and an extra Wednesday, Thursday and Friday; while July 2014 has 4 weeks and an extra Tuesday, Wednesday and Thursday. So if there is lots of extra activity on a Friday, the results of this comparison will be disorted just by how the calendar changes over time. You may think that you are comparing like-for-like when you look at year-on-year movements, but if the data is not seasonally adjusted (including for trading day aspects such as the number of Mondays, Tuesday etc.) then you will get a false understanding of the movements resulting in changes that look important and significant but are just due to the calendar change. So it is important to ensure that seasonally adjusted estimates are used for any year-on-year comparison. Secondly, a year-on-year movement is lagged and tells you nothing about what is happening in the most recent periods. It only tells you what was happening against a year ago! How is that relevant when you’re ignoring the most recent set of information, e.g. May, June, July outputs in any analysis? The economy evolves over time, and a year ago can be a long time in the context of economic activity. Seasonal patterns change over time, and there can be shocks to the economy. To truly understand what is happening now, the most recent time periods need to be considered and taken into account in comparison with the latest data. The best way to do that is to use a form of trend estimates over the most recent time periods.
A response article that touched on these issues raised by the original article was also published and is available here: