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?
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
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
“…evidence that Greece’s economy is still contracting – Greek retail sales tumbled by 12.1% in September, compared with the previous year. That follows a 9.3% decline in August, showing that the slump actually picked up pace.”
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.