The U.S. Federal Reserve wants to get monetary policy back to normal without scaring or surprising the financial markets. Now, try defining “normal,” and you can see it’s going to be difficult.
A vital instrument of abnormal monetary policy has been the promise to keep interest rates at (roughly) zero for an extended period. Once rates have been raised off that floor, this kind of time-based commitment no longer works.
Ordinarily, incoming data would (or should) dictate how quickly interest rates go up. Quite possibly, if inflation fails to rise to its 2 percent target, the data will call for more rate cuts. The point is, nobody knows what the data will say. That’s why normal policy is inherently more confusing than policy at the zero lower bound — hence more capable of springing surprises.
In speeches during the past week, both Fed Vice Chairman Stanley Fischer delved into this.
Fischer:
With respect to forward guidance: its role has been and continues to be important in the long period in which eventual liftoff has been the key interest rate decision confronting the FOMC and the focus of market expectations. However, as monetary policy is normalized, interest rates will sometimes have to be increased, and sometimes decreased.
The challenge, it seems, is to persuade financial markets that policy really will be data-driven.
This week the Financial Times quoted William O’Donnell, a strategist at RBS Securities, expressing a widely held view: “Data dependency and psychoanalysis of the Fed will continue to hold the reins of U.S. rates.” As long as forecasters think the Fed needs psychoanalyzing, there’s a problem with the way it’s communicating.
A Taylor-type rule for monetary policy could help in presenting Fed decisions, even if it wasn’t used to dictate them. Taylor-type rules explicitly link interest rates to inflation and the amount of slack in the economy.
Fischer said that a Taylor-type rule would ignore many factors that ought to influence interest rates, and that the Fed’s policy makers have to use their judgment in reacting to special circumstances — but he also said that it “can provide the starting-point” for decisions. If the Fed leaned more openly on a data-based rule in explaining itself, it would lighten the burden on the markets’ stressed psychoanalysts.
Optimal control is a rule-based method, but much more complex than a Taylor rule. It’s forward-looking and involves minimizing the economic losses predicted by a specific economic model. Everything therefore depends on whether the model in question is any good.
Nonetheless, the two approaches have important things in common: They put structure on one’s thinking and move data center-stage.
Starting-point, baseline, whatever. Policy rules shouldn’t be followed slavishly. Nonetheless, taking them more seriously — and being seen to do so — would help.
