15 February 2012

A Plan for All Seasons

Well, here we go again. The NY Fed's "Empire Manufacturing" survey results were released today, and were good. These are, of course, the seasonally adjusted numbers. Sound familiar? Well, I emailed the NY Fed (yes, one can do that) to ask how it was they do seasonal adjustment, specifically whether the weights are re-calculated each period based on the seasonal factors experienced during the period: weather, events, period length, etc. The answer I got back was to review the methodology, which they publish in some level of detail. I had planned to do that anyway, but a simple "yes" or "no" would have sufficed to answer my question.

Although I didn't get a simple answer, I did get a couple of links to Financial Times discussions of why seasonal adjustment may be a bit wacky these days. For those that don't follow links, the conclusion is that the Fed has adjusted the adjustments (that's a job I'd like: data chiropractor). For those who might not know: seasonal adjustment isn't, so far as I can find, done by measuring data in the period for some set of seasonal factors (weather, holidays, business days in period, etc.), then calculating the period's seasonal weights. Rather, standard practice, since I was in school and long before, is to wave a statistical wand over recent past periods, and coax out seasonality. These links deal with this, possible, bias: the Great Recession happened during autumn and winter (Northern Hemisphere) and the standard algorithms may have bled some part of the Great Recession into seasonality. Using such weights now will boost unadjusted data above where a longer term seasonal adjustment would (or one not using Great Recession data at all; aside, I just got a bit further into the second link, and find this "The second approach is to excise the financial crisis period (specifically, omitting one year of data beginning just before the Lehman bankruptcy) and estimate seasonal factors using this series." Great minds run in the same gutter.). The point being: seasonal adjustment is still fixed weights from past data.

For those interested in what BLS is up to, here's the document.

Or, to quote from the second link, below:
"These biases exist because the computational techniques used to seasonally adjust economic data inappropriately interpreted some of the downturn in the fourth quarter of 2008 as a new seasonal trend."

So, we have two possible upward biases.


Even if you're not an economist or other data life form, it makes for interesting reading. Interesting in the Chinese curse sense, that is.

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