I had a chat with a friend the other day — a prominent academic economist whose name I won’t disclose so he doesn’t get shunned in the faculty room — wherein we bemoaned the state of a) micro-theory (predicts implausible elasticities that never show up in the data; marginal product theory — a core premise — looking ever more suspect*) and b) macro-theory (a terrible muddle these days, as Paul K stresses).
But we agreed that econometrics still rules. Sure, there are those who practice eCONomeTRICKS, but “we regard them with scorn” (extra points for those who can source that quote without Google — even more points for those who can identify why it fits in an econometrics post).
I used to have decent econometrics — statistical analysis of economic data — chops, especially for a former musician/social worker, but alas, no more. I can still reliably run reduced form regressions and the Kalman Filter using the structural (or “state-space”) model I associate with Andrew Harvey (see previous link). But I simply haven’t kept up with the cutting edge stuff, though luckily, I know folks who have.
All of which brings me to the fool’s errand of forecasting employment growth for tomorrow’s jobs report. The consensus is for about 100K. I run a couple of models. At this point in the month, I run a regression of the log changes in payrolls on the lagged quarterly payroll growth, the monthly average of 4-wk UI claims, and the ADP (again, all in log changes) and forecast one month ahead (using the actual UI and ADP data for June).
I also try to tease out the longer-term trend using the Kalman filter on the NSA data — this is a very good way to get at the underlying recent trend, which right now is running at around 90K, which is actually close to what I get with the standard time series regression noted above. So that’s about what I expect tomorrow, though given the confidence interval of 100K around these data along with the monthly revisions, the firm birth/death modeling — well, I don’t know anyone who has a great track record on this one.
However, that’s less a critique of econometrics than a warning about realistic expectations when forecasting high-frequency data.
*The great Joe Stiglitz gave a talk recently at the LSE on his new book on inequality (I also interviewed Joe the other day). Anyway, a bit into the interview, he tells the LSE students, and I’m paraphrasing, “You know, that marginal product theory you’re learning around wage setting — it’s not true … you still have to learn it, but it doesn’t really work.”
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