Slack, inflation, and the Fed

February 15th, 2017 at 12:02 pm

What with Chair Yellen testifying in Congress over the last couple of days, I’ve been trying to dig into the case for forthcoming Fed rate hikes. Clearly, there is more price pressure in the system than in recent months, but there are also these factors to consider:

–The data-driven case for rate hikes is far from a slam dunk, according to evidence I present in the WaPo today.*

–Today’s inflation report is interesting in that it gives ammo to both hawks and doves. Here’s the figure from BLS:

Source: BLS

What’s happening here is that energy costs are normalizing, after being freakishly low for awhile (see figure below). From the perspective of Fed and monetary policy, the key insight here is that energy costs are set on global markets and thus not a reflection of US capacity constraints. That’s why they’ve largely focused on the core, which reveals little acceleration in the above figure. (Dean Baker also pointed out a spike in prices of new cars last month–that’s a monthly anomaly.)

I should also note that the CPI core runs about 0.5 ppts above the PCE core, so think of the Fed’s target for CPI core as ~2.5 percent (as the PCE is their preferred inflation gauge).

Yr/yr energy costs

Source: BLS

So while I understand the case for tightening, and have tried not to get too wound up about small rate hikes, neither do I see a compelling, data-driven case for rate hikes, and that judgement includes this morning’s CPI report.

*For econometric types, there are different ways of running “rolling regressions” of the type I used in the figure in the WaPo piece. Considering that we have T observations (calendar-year quarters in this case), the approach I used estimates the equation using the sample T-n, where n is about half the sample, and then adds back one quarter at time until n is used up. The figure, which plots the coefficient on the slack variable, thus shows how that variable evolves as I add quarterly observations.

But you can also do a “fixed window” approach where you take, say, 20 year samples, for example, and then move the full window up one observation at a time (so, in this case, each regression will have 80 observations). That gives you the picture below (using the same data on core PCE as in the WaPo piece) which tells a similar story re the decreasing sensitivity of inflation to slack.

Source: my analysis (see data note in WaPo piece)

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