Uncertainty is pervasive in the policy environment the Federal Reserve faces as it strives to promote economic stability and low inflation. The economic situation in the U.S. today shows that, even in the best of times, making monetary policy isn’t easy.
- Going with your best guess
- Are costs symmetric?
- Cautionary policy
- Can it pay to be aggressive?
- Can policy cause uncertainty?
Uncertainty is pervasive in the policy environment the Federal Reserve faces as it strives to promote economic stability and low inflation. The economic situation in the U.S. today shows that, even in the best of times, making monetary policy isn’t easy. Is the low unemployment rate a signal inflation is likely to rise soon? Is the stock market boom that seems to be fueling consumption simply a bubble destined to burst? Has the economy entered a period of faster productivity growth? Will a quarter point rise in the funds rate be enough to achieve the Fed’s objectives? The Fed’s policymaking committee, the Federal Open Market Committee (FOMC), must deal with these and other uncertainties in reaching its policy decisions.
In recent years, economists have studied extensively the impact of uncertainty on the conduct of policy. This research has focused primarily on how our imperfect knowledge of the economy affects policy choices, but economists also have studied how public uncertainty about the Fed’s intentions can influence economic developments. This Economic Letter discusses some of the kinds of uncertainties and their implications for monetary policy.
One form of uncertainty that affects policy choices is imperfect knowledge of the economy. A good example of this is the concept of the natural rate of unemployment–the level of measured unemployment that is associated with a balance between demand and supply in labor markets with steady inflation. As actual unemployment falls below this level, the tight labor market leads to wage growth in excess of productivity growth. Firms recoup these higher labor costs by raising prices, which boosts inflation. When unemployment rises above the natural rate, wage growth falls below productivity growth and inflation declines. A central bank with a mandate to keep inflation low and stable will generally want to tighten policy to cool the economy whenever unemployment dips below the natural rate and loosen policy to promote faster growth whenever it rises above the natural rate.
Until the late 1990s, most economists estimated that the natural rate of unemployment was somewhere in the range of 5.5% to 6%. But estimates are the best we can do–we cannot measure the natural rate directly; so our imprecise knowledge about its value is one important source of the uncertainty policymakers face.
If earlier estimates of the natural rate are right, our current low unemployment rate of around 4% is a sign that inflation is likely to start rising. The appropriate response of the Fed in this case is to tighten policy to prevent inflation from rising. But suppose the “new economy” alternative is correct and the economy’s natural rate of unemployment is no longer in the 5.5% to 6% range, but has fallen, perhaps to 4%. In this case, there is no need for the Fed to tighten policy. Doing so would needlessly slow the economy and lead to an increase in unemployment.
How should this uncertainty about the natural rate affect the Fed’s decisions? One approach says it shouldn’t. The Fed should just form its best estimate of the natural rate and then ignore the uncertainty surrounding that estimate–it should act as if it knew the natural rate with certainty. This approach–called certainty equivalence–implies that the Fed should act the same way if it believes the natural rate could be anywhere between 4% and 5% as it would if it believed the natural rate could be anywhere between 2% and 7%. Even though there is much more uncertainty in the latter case, in both cases the certainty equivalence approach says the Fed should act as if it knows with certainty that the natural rate is 4.5%.
Most popular discussions assume certainty equivalence is the way to deal with uncertainty–discussions focus on what our best guesses are for the natural rate, future inflation, or the economy’s trend growth rate while ignoring how much uncertainty surrounds these estimates.
Certainty equivalence allows the policymaking process to take place in two stages. In the first stage, the Fed’s staff can prepare their best forecasts of economic conditions and report these to the members of the FOMC. The members of the FOMC can then make their decisions, acting as if they were completely certain about economic conditions (Clarida, Gali, and Gertler 1999). This approach provides a clean separation between the preparation of forecasts and the making of decisions. But it’s an approach that incorporates an important assumption about the costs of being wrong: certainty equivalence makes sense only if the costs of being wrong do not depend on whether one overestimates or underestimates the natural rate. If the staff tells the FOMC that the best estimate of the natural rate is, say, 4.5%, the FOMC can ignore the uncertainty surrounding that estimate only if they will be equally unhappy if the true natural rate turns out to be 7% or if it turns out to be 2%.
In many situations, the costs of making a mistake are not symmetric. Consider your actions when you have a plane to catch and the flight is scheduled to depart at 6 p.m. The time it takes to get to the airport is uncertain because of possible traffic delays. Arriving twenty minutes early does not have the same consequences as arriving even five minutes late. In one case, you have a few extra minutes to kill at the airport; in the other, you may miss an important meeting or have to stay over an extra night. In this case, most people leave a bit earlier for the airport than they believe is absolutely necessary–if your best guess is that it takes an hour to get to the airport, you may decide to leave yourself an hour and fifteen minutes to get there. Uncertainty about the drive to the airport affects your decision about when to leave.
How does this apply to the Fed’s decision? The Fed must assess the costs that would arise if it underestimated or overestimated the natural rate. Consider the costs of underestimating the natural rate. Suppose the Fed’s best guess of the natural rate is that it has fallen to around 4%, equal to the current actual unemployment rate. What are the costs if the Fed has underestimated the natural rate versus overestimating the natural rate? If the natural rate is really still much higher than 4%, a failure to tighten policy now risks a significant increase in inflation. If the natural rate has fallen even lower, to, say, 3.5%, then a tightening leads in the short run to unnecessarily high unemployment and its associated lost income. Are these two outcomes equally costly? That is the question policymakers need to grapple with. Only if the costs of errors are symmetric can policymakers ignore the degree of their uncertainty about the natural rate.
While uncertainty about the natural rate provides a useful illustration of one type of uncertainty policymakers face, it isn’t the only type the Fed faces. The Fed also cannot be sure of exactly how its actions will affect the economy. Milton Friedman described one aspect of this uncertainty in terms of the “long and variable” lags between a change in policy and the time at which it finally influences the economy. Will a quarter point increase in the funds rate, such as the FOMC decided upon in February, be sufficient to prevent the economy from overheating? Or will that increase have too small an impact, so that further rate increases will be needed? And how does this sort of uncertainty affect the implementation of policy?
Over 30 years ago, William Brainard of Yale University (Brainard 1967) provided an answer. Imagine you are driving a car. Sometimes when you turn the steering wheel, the car barely responds; at other times, the slightest adjustment of the steering wheel produces a sharp change of direction. How should you drive? Very cautiously, according to Brainard. When a policy action’s impact on output and inflation is hard to predict, it becomes optimal to respond cautiously to new developments.
Alan Blinder, a former Vice Chair of the Board of Governors, has argued (Blinder 1998) that Brainard’s result accorded closely with what Blinder felt was a reasonable approach to policy–calculate the best change in the federal funds target as if one faced no uncertainty, and then change the funds rate a little less.
Uncertainty can lead to caution. Can it ever lead to more aggressive policy actions? Recent research has suggested the answer to this question is yes.
Suppose you are driving along a narrow ridge, buffeted by gusting winds, and the linkage between movements in your steering wheel and the car’s response is hard to predict. A strong blast of wind could push you over the edge. You don’t know where the next gust will come from. Does it make sense to respond cautiously if the wind suddenly pushes to the left? No–respond too cautiously and you might end up over the edge if the car fails to respond when you turn the wheel slightly. Better to risk over-steering than to find yourself over the edge.
In this example, uncertainty about the response of the car to your attempts to steer would lead you to respond strongly rather than cautiously. In doing so, you might have the best chance of avoiding a really bad outcome. Similarly in monetary policy, aggressive interest rate movements might be called for in order to avoid a major economic disruption (Giannoni 1999).
The examples so far have involved uncertainty about the economy or about the impact on the economy of policy actions. But policy decisions themselves also can contribute to economic uncertainty. During the 1980s, when central banks in many countries were striving to reduce inflation, the public’s expectations about inflation often remained stubbornly above the inflation rates the central banks claimed they were going to achieve. The public was uncertain about whether policymakers really were committed to getting inflation down. This uncertainty led the public to continue to expect inflation, and that raised the unemployment costs of bringing inflation down.
In today’s low-inflation environment, people still face uncertainty about policymakers’ actions. Will the Fed wait until inflation starts to rise before tightening? Will it act preemptively to ensure low inflation is maintained? Will recent interest rate increases be followed by further ones over the next few months? To reduce this type of uncertainty, many central banks have begun to issue detailed inflation forecasts or to announce inflation targets publicly. The intention is to provide more information about policy objectives to the public.
Economists have studied in detail the implications of uncertainty for the conduct of monetary policy. Unfortunately, uncertainty comes in more than one flavor, and the appropriate policy response depends on the costs it imposes. In the certainty equivalence approach, uncertainty can be ignored. At the same time, in some cases, policy should respond less strongly, while in others, it should respond more strongly. And finally, some of the uncertainty in the economy may even be traced to the public’s uncertainty about the future course of policy. Uncertainty is pervasive, but there is no one answer about how to deal with it.
Carl E. Walsh
Professor of Economics, UC Santa Cruz
Visiting Scholar, FRBSF
Blinder, Alan. 1998. Central Banking in Theory and Practice. Cambridge: MIT Press.
Brainard, William. 1967. “Uncertainty and the Effectiveness of Policy.” American Economic Review 57, pp. 411-425.
Clarida, Richard, Jordi Galí, and Mark Gertler. 1999. “The Science of Monetary Policy: A New Keynesian Perspective.” Journal of Economic Literature (December) pp. 1,661-1,707.
Giannoni, Marc. 1999. “Does Model Uncertainty Justify Caution? Robust Optimal Monetary Policy in a Forward-Looking Model.” Mimeo. Princeton University, November.
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