Excellent resource for seasonal adjustment related articles

For those in the know, the United States Census Bureau website is a good resource for technical seasonal adjustment papers.

Good to see legends of the field in David Findley and Agustin Maravall still getting their expertise and knowledge out there.

Link: http://www.census.gov/srd/www/sapaper/sapaper.html

“lluminating Model-Based Seasonal Adjustment with the First Order Seasonal Autoregressive and Airline Models, by David F. Findley, Demetra P. Lytras, and Agustin Maravall (CSRM Research Report, 2015”

Wavelet benchmarking

Wavelets are the way forward. It will be interesting to eventually see a wavelet approach to seasonal adjustment, but in the mean time, here is some research work for the use of wavelets to benchmark time series. The title is “An Introduction to Applications of Wavelet Benchmarking with Seasonal Adjustment”.

The full article is available here: http://arxiv.org/abs/1410.7148

In it they note that: “The versatility of the procedure is demonstrated using simulation studies where we provide evidence showing it substantially outperforms currently used methods. Finally, we apply this novel method of wavelet benchmarking to official Office of National Statistics (ONS) data.”

Cutting edge stuff!

Choose the right indicator for analysis

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:


Using Prezi for presentations

Finally found some time to revisit and review some previous training material that was laying around! The prezi presentation approach is pretty good and a different take on the standard slide packs. Check out a short set of slides on different types of time series.

Any comments or suggestions more than welcome.

Light reading of a manual…

There is a lot of good technical and detailed stuff hidden away in the international manuals if you know where to look.

The IMF has a Quarterly National Accounts manual which they are in the process of updating. A full link is available here: http://www.imf.org/external/pubs/ft/qna/.

Chapter 7 has some detail on the specifics relating to seasonal adjustment. Check it out here: http://www.imf.org/external/pubs/ft/qna/pdf/chapter7.pdf.

Useful guidance on communicating uncertainty

The Government Statistical Service in the United Kingdom have put out some useful guidance on communicating uncertainty. You can check it out here:


The most interesting part on page 4 is they say

“You should provide sufficient and appropriate information to indicate:
…a longer term view of change (e.g. trend)”

Good to see the trend get an official mention as when it is packaged with a range of other indicators (original and seasonally adjusted estimates), it can give a complete understanding of the nature of the time series. Why settle just for the seasonally adjusted estimates when it still contains the noisy part of the time series?