This morning, I tweeted out Spencer Jakab’s WSJ column on NFP — It’s a Hard Job Predicting Payrolls Number, with the annotation “Its pointless, too.”

While I understand the obligation many economists have to their employers to make a jobs forecast, you have no such obligation. You don’t have to make a prediction, weigh in, make a guess, create a forecast model or even read other people’s forecasts.

Why not?

Here are three reasons:

1) People are really, really bad at making accurate forecasts:  Most forecasts are at best, an educated hypothesis and at worst, a blind guess. A glance at the history of these sorts of predictions reveals that everyone gets these things wrong. I have yet to see someone consistently forecast these things. Indeed, I have yet to see a good 3 month in a row streak forecast by any economist. We simply lack the ability to predict the future.

2) Modelling isn’t much better: The combination of a huge number of known variables, poor data assembly, and a number of unknown variables — plus a healthy dollop of unforeseen randomness — makes employment data forecasting at best slightly better than raw guessing.

3) Even if you could make an accurate forecast, it wont help you in the markets: That’s the funny part of all this — it is a meaningless exercise for investors, and a dubious one for traders. This is especially true in the present investment environment where the FOMC looms as large as they do. The next level analysis is whether the good news is bad (meaning less accommodation) or good (economic improvement) or conversely where bad news is good (meaning more accommodation) or bad (economic deterioration).

Our time would be better utilized trying to discern the current state of the labor market — what actually is (and recently was) rather than what might be. This is useful data for companies, policymakers and labor participants. It has actual utility. Predictions don’t.

Source: The Big Picture