USA retail sales and trend estimates for August 2015

It has been a long time since we’ve looked at the USA Retail Sales estimates. Way back in 2012: http://www.seasonaladjustment.com/2012/09/14/usa-retail-sales-for-august-2012-and-the-trend/ so it is worth a revisit.

The Census Bureau do not estimate or publish official trend estimates, but trend estimates can be derived by taking the published seasonally adjusted estimates and applying a set of Henderson filters (with a bit of code in R and ggplot2). Using the latest published data up to and including August 2015 gives

USA Retail Sales Seasonally adjusted and trend estimates

where the one month percentage change in the trend and seasonally adjusted estimates are

Dec 2014 Jan 2015 Feb 2015 Mar 2015 Apr 2015 May 2015 Jun 2015 Jul 2015 Aug 2015
Trend -0.24 -0.18 -0.01 0.26 0.49 0.58 0.53 0.45 0.36
Seasonally adjusted -0.87 -0.77 -0.53 1.54 0.03 1.18 -0.04 0.71 0.19

So underlying one month movement in the trend has been strong since March 2015 even though the seasonally adjusted one month movements have bounced around. Even with a dip in the seasonally adjusted estimate in September 2015, it shouldn’t change the fundamental view of the underlying strengh in recent periods.

Over the length of the series the median for the one month percentage change in the trend for USA retail sales is 0.4%, so the recent activity is back in line with historical growth.

For background you can get the seasonally adjusted data here: http://www.census.gov/retail/marts/www/timeseries.html

Example of the use of trend for volatile series

A good example of how the use of trend estimates can help when the estimates are volatile.

Link: https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/206348/bovinetb-statsnotice-12jun13.pdf

Example trend bovine

Monthly data can be volatile

Great article below by Tim Colebatch who is The Age’s economic editor.

He has correctly highlighted that one months worth of data can be volatile and that to get a better picture it is better to looked at the smoothed data (e.g. the trend). This is obviously series dependent and some data will be more volatile than others. So it is a case of knowing your data and its characteristics.

In practice it is often the case that analysts and users can get caught up in the moment as they often want to grab at the latest information to prove (or disprove) their own or their opponents arguments. What should happen in practice is precisely what Tim Colebatch has described.

Rather than get excited (or not) based on just one months data, take a deep breath and look at the pattern over a few time points (in this case the smoothed data after calcuating a trend).

The [Australian Bureau of Statistics] keeps warning us that its monthly job movement figures are too imprecise to rely on, and urges us to use its smoothed trend data instead.
Pity the Treasurer and the Reserve don’t listen.
And the trend figures this month tell us pretty much what they told us last month: there’s virtually no job growth going on out there.