Regional President Speech

Last Updated: 03�20�15 A speech delivered on March 20, 2015, at the Brookings Panel on Economic Activity in Washington, DC� In a speech to the Peterson Institute I gave last September, I argued that the Federal Open Market Committee �FOMC� would be well served by being especially cautious before raising the fed funds rate o� zero� In this paper, Jonas Fisher, Françcois Gourio, Spencer Krane and I develop explicit theoretical and empirical foundations for this argument� We see two substantial and con�icting risks facing policymakers today� Suppose that the FOMC raises rates, but then discovers that the economy is more reliant on policy accommodation than previously thought — either growth turns weaker or in�ation remains stubbornly low� In this situation, the Fed could �nd itself forced back to the zero lower bound �ZLB�� The unconventional tools we have to provide accommodation at the ZLB are useful, but they are imperfect substitutes for changes in the funds rate� And these unconventional tools may be less e�ective going forward if a premature exit reduces the public’s con�dence in the Fed’s commitment to its goals� The other substantial risk is higher in�ation� Suppose the FOMC delays raising rates so long that in�ation rises much faster than currently projected� Well, the Fed knows how to respond to that — we can raise rates to reel in�ation back in� And if the economy is on a solid footing, these rate increases should be manageable� To me, this suggests we should err on the side of less aggressive policy tightening� We call the �rst channel the “expectations channel�” We explore this using the standard workhorse forward-looking New Keynesian model� In that model, when the ZLB binds, both output and in�ation are low� So the chance that the ZLB will bind tomorrow translates into lower expected output and in�ation tomorrow� And because agents are forward looking, the expected losses tomorrow reduce output and in�ation today through negative wealth e�ects and lower in�ationary expectations� In this situation, what should the policymaker do? Our analysis shows that optimal policy under discretion is to loosen policy now� We call the second channel the “bu�er stock channel�” We explore this using an “old-style Keynesian” framework, in which growth and in�ation have some inherent underlying momentum due to inertia� This is summarized by the output gap and in�ation being functions of their own lagged values� In this setup, the higher output and in�ation are today, the higher output and in�ation will be tomorrow, reducing in turn the chances that the ZLB will bind tomorrow� Suppose there is a signi�cant chance a shock tomorrow will push you towards the ZLB� We show that policy should be looser today to build up a bu�er of in�ation and output so that if that shock does occur, you are less likely to actually hit the ZLB tomorrow� And even if you do get pushed to the ZLB tomorrow, the bu�er lowers the costs of doing so� Naturally, there is a cost from this policy if the shock does not occur and the economy “overheats�” The resulting in�ation will have to be brought down� Optimal policy under discretion recognizes these costs and balances them against the bene�ts that the bu�er provides against the ZLB� Our theoretical analysis formally develops these two channels� We have two propositions — one for each model� But they essentially amount to the same thing: As uncertainty rises regarding future shocks that can drive policy to the ZLB, the looser today’s policy should be to guard against those events� This policy amounts to taking out insurance today against the risk of hitting the ZLB tomorrow� So these propositions certainly �t into a common-sense view of risk management� For analytical convenience and clarity, we present the two channels independently in di�erent simple models� However, both channels operate in standard macro models, such as the state-of-the-art dynamic stochastic general equilibrium �DSGE� models� My time is short, so I will brie�y illustrate some of what we �nd using a very standard backward-looking model� In this model, optimal policy is the interest rate chosen to minimize the usual quadratic loss function of the deviation of in�ation from target, π, and the output gap, x� This choice is made subject to being constrained by the ZLB and taking into account private sector behavior, which is governed by the backward-looking Phillips and investment�saving �IS� curves� All of the uncertainty in the model surrounds the cost-push shock denoted by ut in the Phillips curve and shocks to the equilibrium “natural” real interest rate denoted by ρn in the IS curve� We simulated this model using standard parameter settings from the literature� In period zero, the output gap is –1-1�2 percent, in�ation is 1�3 percent �the latest reading for the price index for core personal consumption expenditures, or PCE�, and the natural rate of interest is –1�2 percent� We then assume the real natural rate rises gradually to its long-run level of 1-3�4 percent over the next four years� Uncertainty is modeled as �rst-order autoregressive, or AR�1�, shocks about this natural rate path and in the cost-push term� With these settings plugged into the model, the optimal policy has the nominal interest rate at the zero lower bound in the �rst period� The red line in the upper right panel shows the optimal interest rate as we march through time� Obviously both the private sector and the Fed react to shocks as they hit the economy, and there are many possible outcomes� This line is a “baseline scenario,” in which agents make decisions taking into account the model’s uncertainty, but ex post all shocks turn out to be zero� This is akin to plotting an impulse response path� Note that optimal policy delays lifto� from the ZLB for six periods� The resulting output gap is shown by the red line in the lower left panel� The gap falls immediately from –1-1�2 percent at time zero to –1 percent at time one� The output gap eventually overshoots zero, but this is needed to bring in�ation — the red line in the lower right panel — back up to target and to maintain a bu�er against the ZLB� For the sake of comparison, we plotted the blue lines, which show what the 1993 Taylor rule would generate� The Taylor rule would immediately set the nominal rate to 2 percent� Output takes four years to return to its potential level, and in�ation remains low for a long time� Finally, the green line in the upper right panel shows what optimal policy looks like if uncertainty increases by 50 percent� Consistent with our theorem, optimal policy in this scenario has lifto� delayed by a further six quarters� Of course, the baseline scenario is just one possible realization out of many� The next slide considers a scenario that exhibits what many think of as the biggest risk to delaying lifto� — in�ation could take o� to unacceptably high levels� Here we assume that two large back-toback cost-push shocks lead to a burst of in�ation before what would otherwise be the time for lifto�� These shocks stand in for some unexplained jump in in�ation expectations or for other in�ationary forces beyond those captured in the model’s simple Phillips curve� You can see in the upper right panel that both optimal policy and the Taylor rule hike rates aggressively in response to these shocks� Optimal policy clearly does a much better job in closing the output gap than the Taylor rule� And while the Taylor rule does a better job bringing in�ation back to target, the in�ation path under optimal policy is not radically di�erent� In other words, delaying lifto� does not seem to seriously impair the Fed’s ability to hold in�ation in check� This table summarizes the outcomes from a large number of simulated paths of the natural rate and cost-push shocks using the same set of initial conditions as before� Optimal policy has the smaller loss — about 1�2 of that under the Taylor rule� We �nd it revealing to look at the economic conditions under which lifto� occurs� In the typical, that is, median, draw, optimal policy lifts o� with a small positive output gap and in�ation essentially at target� This looks like a “whites of their eyes” in�ation-�ghting policy� The optimal policy greatly reduces the odds of hitting the ZLB tomorrow — the probability that the natural rate is negative the quarter after lifto� is only about 20 percent under optimal policy� These outcomes are very di�erent than under the Taylor rule, which typically lifts o� with large negative gaps and 90 percent of the time runs into a negative natural rate immediately after lifto�� How well do the policies guard against bad luck? At the bottom, we see the median worst outcomes across simulations� Optimal policy and the Taylor rule are not very di�erent here — worst-case output gaps of 1 percent or so and in�ation rates in the range of 4 percent� These results reinforce the point we noted from the previous chart: Optimal policy is able to raise rates enough to choke o� in�ation scares — and can do so essentially as well as the Taylor rule� How has risk management entered into actual FOMC policymaking? In our empirical analysis, we study a time span when the funds rate is mostly well above zero� Nevertheless, this history is still worth investigating: If the Fed has practiced risk management away from the ZLB, it seems reasonable for it to take account of the special risks generated by the ZLB in its decision-making calculus today� We study risk management empirically by estimating standard Clarida-Galí-Gertler-style monetary policy reaction functions� Here R-star is a notional target funds rate that depends on the expected di�erence of in�ation from its target and the expected average output gap over the next year� We use the Fed’s Greenbook forecasts for these variables� We then append a variable st that proxies for risk-management factors that might in�uence the policy decision over and above how they might a�ect the forecast� As is standard, we assume a partial adjustment of the actual funds rate to this notional target� We consider an eclectic mix of risk-management proxies� The FOMC’s minutes and other Fed communications suggest risk management was often a consideration a�ecting policy moves� There are numerous references to uncertainties over the outlook; insurance against skewed losses; or preemption of nascent recessionary dynamics� We summarize this information by coding dummy variables to indicate when we judged uncertainty and insurance considerations shaded policy higher or lower than prescribed by the forecast alone� We also let the computer take a shot at this, by having it count sentences in the minutes that mention “uncertainty” and “insurance�” Other proxies include revisions to Greenbook forecasts for gross domestic product �GDP� and in�ation — large forecast revisions may signal asymmetric weights on outcomes in the direction of the shock and the FOMC may have wanted to insure against these outcomes� We also consider variables that summarize variance and skewness in private sector forecasts, including those derived from �nancial markets and those derived from the Philadelphia Fed’s Survey of Professional Forecasters �SPF�� Here are the variables we found statistically signi�cant at the 5 percent level� When the human-coded uncertainty dummy turns on up or down, the notional target funds rate changes by 40 basis points� All other numbers are the basis point responses to the notional target associated with a one standard deviation increase in a variable� Overall, we �nd what our historical narrative analysis also suggests: Before it was constrained by the ZLB, the FOMC seems to have incorporated risk-management considerations into its policy decisions� This chart shows how one of our proxies — the variance across the GDP point forecasts made by the SPF panelists — lines up with the residuals from the policy reaction function that excludes a risk-management proxy� We see a clear negative correlation, suggesting the Committee shaded down the funds rate during times when the private sector — and, presumably, the Fed — had heightened uncertainty over the outlook for growth� It’s useful to look more closely at the 2000–01 period� The bars in this �gure show the e�ect on the notional funds rate of the GDP point forecast variance shown in the previous �gure� The bars are quarterly because this is the frequency of the SPF� By the middle of 2000, the economy appeared to be slowing, in part because of monetary tightening in 1999 and early 2000� The level of our proxy for uncertainty usually was below average then� But as we moved through 2001, the economy slowed more, and uncertainty grew about whether we were slipping into recession — this shows up here as negative e�ects of the uncertainty variable� Then, the tragic events of September 11th hit, which created huge uncertainty and downside risks to the economic outlook� We see this in the spike in the SPF uncertainty variable in November — the last bar — that would point to a nearly 100 basis point drop in the funds rate� The red line plots the actual path for the funds rate against these uncertainty e�ects� We can see that the Fed began to undo its tightening cycle in mid-2000, and cut rates a good deal further in mid-2001as uncertainty rose� We then see that the large spike in uncertainty at the end of the year is correlated with a big drop in the funds rate� Of course, there is much more to be said about risk management during this period� The 2000 pause in rate increases occurred despite forecasts of very low unemployment and rising in�ation, in part, as the minutes tell us, because the Committee was uncertain over the degree to which its previous tightening might further a�ect the economy� The aggressive rate cuts in early 2001 encompassed other rationales; notably, the January 30–31, 2001, minutes stated the Committee was front-loading easing to “help guard against cumulative weakness” — perhaps a reference to policy insurance against outcomes skewed to the downside� The post-9-11 cuts had this reasoning as well� In addition, as evidenced in the November 6, 2011, minutes, the Committee noted its concern that recessionary dynamics could, and I quote, “be di�cult to counter with the current federal funds rate already quite low�” Accordingly, the large policy moves could also have re�ected insurance against the future possibility of running into the ZLB — precisely the policy scenario our theory addresses� So, to conclude, we think our theoretical and empirical analysis support one of my favorite quotes from one of our discussants: 

Charles L. Evans (2015, March 19). Regional President Speech. Speeches, Federal Reserve. https://whenthefedspeaks.com/doc/regional_speeche_20150320_charles_l_evans

@misc{wtfs_regional_speeche_20150320_charles_l_evans,
  author = {Charles L. Evans},
  title = {Regional President Speech},
  year = {2015},
  month = {Mar},
  howpublished = {Speeches, Federal Reserve},
  url = {https://whenthefedspeaks.com/doc/regional_speeche_20150320_charles_l_evans},
  note = {Retrieved via When the Fed Speaks corpus}
}