Thursday, February 14, 2019

JOLTS February 2019 (Dec 2018 data)

Sorry about the delay in adding the latest data to the forecast plots, but unfortunately something has happened in my upgrade to Mathematica 11.3 that's broken the single prediction errors on some of the JOLTS graphs. I've been trying to figure it out. Overall, it's another "status quo" data release with data basically in line with the models. The openings turned out fine, but the errors for the projection for the recession counterfactual broke — I left it off:

As a side note — if there's another major data revision in the same direction as the data release around the Fed's March meeting in 2018, the negative deviation could largely go away.

My guess for the source of the problem is that the treatment numerical precision was updated such that the silly coefficients in the single prediction errors started being treated properly in terms of precision instead of being cancelled algebraically. Silly coefficients? Yes — how's 7.306167312812677×101813 for you? I think the source is the years — 2016 is unnatural in many senses. Yes, I know, I should have subtracted out 2000 before putting them through the fitting algorithms. But it worked fine up until this week. I guess my hard drive has worked fine up until this week, too ...

In case you're interested, here's how some of the other graphs turned out:

The Mathematica 11.3 upgrade came along with a transition to Windows 10, so I can't just revert back to 11.2. I may have to move these to my old computer with Mathematica 10 point something.


Update 15 February 2019

Back to Mathematica 10.3, and we're up and running again (click to enlarge):

And here's the alternative Hires model based on this collection of dynamic equilibrium relationships:

The hires data still doesn't show a deviation. Based on this model which puts hires as a leading indicator, we should continue to see the unemployment rate fall through May of 2019 (5 months from December 2018, which is the data that was released this week).

Friday, February 1, 2019

Unemployment ticks up

I think this is the longest I've gone without blogging since I started my blog! It's due to a confluence of factors — writing my second book (now about 1/2 written, and I'm learning so much finally looking at all these dynamic equilibrium models together from the 30,000 foot level), and I've become ridiculously busy at my real job. It means I missed the 2nd anniversary of the dynamic information equilibrium model. The model came out as a series of posts, first for JOLTS data (10 Jan 2017), then for the unemployment rate (11 Jan 2017), and finally a forecast of the unemployment rate (18 Jan 2017). That last forecast was compared with a forecast from the FRBSF that went until the end of 2018 — it was flat at about 4.7% the entire period. As I said in that last post:
The interesting thing is that in either [dynamic equilibrium] model we shouldn't expect the flattening out over the two year period [Dec 2016 to Dec 2018] we see in the FRBSF model. We should expect either 1) a recession to start raising unemployment, or 2) a continuing decline (albeit at a slower rate). A constant unemployment rate won't happen, and in fact generally doesn't happen [1]. We might be able to test the various models here [2].
And we could!

The uptick in the latest data to 4% is being blamed on the US government shutdown in the business news, but it's really well within the normal fluctuations of the unemployment rate so any speculation as to its cause is pretty much unfounded without detailed micro data. As a side note, there are various reasons why people think the Fed paused its rate increases at it's meeting this past week — discussed thoroughly by Frances Coppola here. However, I think Tim Duy has the best theory of the Fed's reaction function: as long as inflation isn't a problem, the Fed doesn't hike rates if unemployment is flat. 

The most recent data shows a relatively flat unemployment rate for the past few months, much like in 2016. Basically, the FOMC has some model like the FRBSF model in its collective head of a constant equilibrium unemployment rate. And it's precisely that model which led the FRBSF forecast astray.