If you’re a user of R then check out this package that interfaces to the X-13-ARIMA-SEATS executable.
A good source of information related to theoretical and practical issues for seasonal adjustment is the United States Census Bureau website.
Check them all out here: http://www.census.gov/srd/www/sapaper/sapaper.html
A good example of how the use of trend estimates can help when the estimates are volatile.
I know many of you have been waiting for this and here it is!
New versions of X-13-ARIMA-SEATS have just been released by the United States Bureau of the Census. Check them all out in the following links.
The Bureau of the Census also has a lot of good seasonal adjustment resource information to check out.
We have been very busy here and unfortunately there has not been an opportunity to post any updates. Better late than never… we wish you a productive 2013.
I hope you are not SAD and have seasonal adjustment disorder.
Today the Greek retail sales were released and it made some headlines with
The actual data can be obtained from the ELSTAT website although it doesn’t seem that the seasonally adjusted estimates are made available. This probably explains why the news reports focused on the 12.1% fall between September 2011 and September 2012. But having downloaded the original data we can apply seasonal adjustment (and also calculate trend estimates) by using X-13-ARIMA and see if this changes the interpretation of the most recent data. Plotting this shows…
Some interesting observations.The year-on-year change in September is -12.1% in the original data for September (as reported), but once seasonality is taken into account (e.g. in this case the changing seasonality over the years), this is -11.9% over the year. Calcuating a trend estimate gives the underlying change over the last year of -10.4%.
Having derived the seasonally adjusted and trend estimates this can give us a better indication of what is happening at the current end of the series. While the month-on-month trend changes are still decreasing in the recent six months, the latest data shows a -1.1% change in the trend between August and September 2012. This is a much better indicator of what is going on now than looking at the year-on-year change in the unadjusted data.
Interesting, the plot also shows that the December seasonality is reducing quickly over the last few years. This is more easily seen by looking at the seasonal plot for December below. I have also included August and September data. Some of the downwards monthly movement that is seen for September 2012 can be attributed to the higher than normal August estimates, as the September 2012 data came in pretty much inline with historical September estimates. So perhaps the recent Retail activity is starting to level out.
A relatively obscure journal (well obscure in the sense that it is not main stream and if you weren’t aware of its existence you might not know about it) has put out a set of articles on seasonal adjustment for a special edition.
The journal is the Taiwan Economic Forecast and Policy, and it is Volume 43, No.1 (October 2012). You can access it here: http://www.econ.sinica.edu.tw/academia-02.php. Not all papers seem available for download but you can get access to a few of them.
Some of the papers have been presented at the 2010 Macroeconometric Modelling Workshop on Seasonal Adjustment Methods held during December 9–10, 2010 at the Institute of Economics, Academia Sinica, in Taiwan.
As we’ve mentioned previously, different indicators such as month-on-month, or year-on-year, can provide mis-leading interpretation in times of economic change. One of the papers – (update: which is now available for download) – is by leading seasonal adjustment experts Benoıt Quenneville (Canada) and David Findley (USA). They look at when a time series being measured contracts sharply for a few months and then starts to recover, and how the publication of both the annual and monthly growth rates can give conflicting signals. They show the commonsense fact that in this case the year-on-year ago comparison has a phase shift of around six months and that the month-on-month comparison has a phase shift of half a month. In practice, the use of indicators like this introduce phase shifts in the derived indicators which can delay the detecting of turning points and then lead to mis-interpreting the direction of the series. So basically, year-on-year changes is a bad thing. Memorise this.