RDP 2017-06: Uncertainty and Monetary Policy in Good and Bad Times 5. Uncertainty and Monetary Policy

5.1 Systematic Monetary Policy Effectiveness

The previous evidence shows that US monetary authorities react to uncertainty shocks in both phases of the business cycle. But what would have happened if the Federal Reserve had not reacted to the macroeconomic fluctuations induced by uncertainty shocks? Would the recessionary effects of such shocks have been magnified? Answering these questions is key to understanding the role that conventional monetary policy can play in tackling the negative effects triggered by sudden jumps in uncertainty.

To answer these questions, we run a counterfactual simulation using our STVAR. Our counterfactual exercise assumes that the central bank does not respond to an uncertainty shock, that is, we shut down the systematic response of the federal funds rate to movements in the economic system triggered by uncertainty shocks. Following Sims and Zha (2006), we do so by zeroing the coefficients of the federal funds rate equation. Implicitly, in this counterfactual economic agents are repeatedly ‘surprised’ by the failure of monetary policy to respond to the uncertainty shock in its accustomed way, which raises well-known issues highlighted by the Lucas critique. However, for deviations of policy from its historical pattern that are neither too large nor too protracted, our estimates of the policy effects provide reasonable counterfactual approximations (Sims and Zha 2006).

Figure 6 compares the reaction of real activity conditional on the absence of a systematic monetary policy response to the baseline results. The results suggest that the effectiveness of systematic monetary policy is lower in recessions. In other words, the recession is estimated to be as severe as the one that occurs when we allow policymakers to lower the policy rate. Notably, the difference between the baseline and the counterfactual scenarios mainly regards the speed with which real activity recovers and overshoots before going back to the steady state. Possibly this is due to the lags via which monetary policy affects the real economy.

A different picture emerges in good times. As Figure 6 shows, when the policy rate is kept fixed, industrial production goes down markedly (about 2 per cent at its peak) and persistently, remaining statistically below zero for a prolonged period of time.^[17] The same holds when looking at the response of employment, that is, the gap between the baseline response and the one associated with our counterfactual scenario is quantitatively substantial in expansions. This suggests that monetary policy plays an important role in reducing the probability of entering a recession if the uncertainty shock occurs in good times. But it doesn't make much difference if the economy is already in a recessionary state.

5.2 Interpreting Policy (In)effectiveness in Recessions

How can one interpret the state-dependence of monetary policy effectiveness? As suggested by Bloom (2009) and Bloom et al (2014), these findings might find a rationale in the real option value theory. When uncertainty is high, firms' inaction region expands as the real option value of waiting for new information increases (Bloom 2009). In recessions, it could be that the ‘wait-and-see’ behaviour becomes optimal for a larger number of firms compared to normal times. If the real option value of waiting is high, firms become insensitive to changes in the interest rate, which explains why the peak recessionary effect is virtually identical regardless of the reaction of monetary policy. When uncertainty starts to drop, the inaction region shrinks, firms become more willing to invest and face their pent-up demand. In turn, the elasticity of investment with respect to the interest rate starts increasing. If monetary policy does not react, as in our counterfactual scenario, the higher (relative to the baseline) cost of borrowing starts playing a role. Hence, firms re-start investing at a slower pace. In the medium run, once uncertainty has vanished, firms invest less with respect to the baseline case, and the overshoot is substantially milder, if at all. A similar reasoning applies to labour demand and, therefore, employment.

Differently, the response of monetary policy has a larger countercyclical effect on the downturn triggered by uncertainty shocks in expansions. If the option value of waiting due to uncertainty is lower in expansions compared to recessions, firms are more reactive to changes in factor prices. Hence, if the nominal interest rate remains unchanged, investment is likely to be lower. Consequently, uncertainty shocks trigger stronger contractionary effects in absence of systematic monetary policy interventions.

These findings line up with those in Vavra (2014), who shows that monetary policy shocks are less effective during periods of high volatility. In his model, despite the presence of an inaction region due to price adjustment costs, second moment shocks push firms to adjust their prices more often. This increased price dispersion translates into higher aggregate price flexibility, which dampens the real effects of monetary policy shocks. Given the countercyclicality of price volatility, monetary policy turns out to be less powerful in recessions. A similar mechanism is present in Baley and Blanco (2016). Our results complement Vavra's and Baley and Blanco's, because we show that the systematic component of monetary policy is less effective in recessions.

Berger and Vavra (2015) build up partial and general equilibrium models that focus on the response of aggregate durable expenditures to a variety of macroeconomic shocks. In particular, their model features microeconomic frictions that lead to a decline in the frequency of households' durable adjustment during recessions. This decline in the probability of adjusting during recessions, in conjunction with the variation over time in the distribution of households' durable holdings, implies a procyclical impulse response of aggregate durable spending to macroeconomic shocks, a result also documented in Berger and Vavra (2014). Hence, macroeconomic policies are less effective in stabilising the business cycle (at least, durable spending) in recessions, consistent with our counterfactual impulse responses.

Our empirical findings are also consistent with those by Weise (1999), Aastveit et al (2013), Mumtaz and Surico (2015), Tenreyro and Thwaites (2016), Eickmeier et al (2016), and Pellegrino (2017a, 2017b), who also find monetary policy to be less powerful in periods of high uncertainty or, more generally, during recessions. In particular, Mumtaz and Surico (2015) use quantile regression techniques to estimate a nonlinear empirical model of consumption, in which the conditional quantile distribution of consumption is a function of the real interest rate and leads and lags of consumption itself. They show that, when real activity is above average, the degree of forward-lookingness and the interest rate semi-elasticity are significantly larger than the values estimated when real activity is below average. This implies that, all else being equal, monetary policy is more powerful in good than in bad times. Given the tight link between the IS schedule (which refers to the consumption/saving decisions by households) and the financial markets, our results might also be seen as consistent with the different role played by financial frictions in economic booms and busts.

We note that, as is the case for any given counterfactual simulation, the accuracy of our results on the effectiveness of systematic monetary policy depends on all else being equal. Our counterfactual is a ceteris paribus exercise in that the only difference between our baseline results and the ones obtained with the counterfactual simulation is the response of the federal funds rate to the uncertainty shock. However, we cannot rule out the possibility that the nonlinear effects of systematic monetary policy uncovered in our analysis are also capturing nonlinearities related to other macroeconomic policies, most notably fiscal policy.^[18]

5.3 Risk Management by the Federal Reserve

The evidence provided so far shows that uncertainty shocks trigger a response by monetary policymakers, and that this response is particularly strong during recessions. But what role did uncertainty per se play as far as the US monetary policy setting is concerned? In analysing the conduct of monetary policy under his regime, Greenspan (2004, pp 36–37) states that:

The Federal Reserve's experiences over the past two decades make it clear that uncertainty is not just a pervasive feature of the monetary policy landscape; it is the defining characteristic of that landscape … the conduct of monetary policy in the United States has become to involve, at its core, crucial elements of risk management.

While being consistent with Greenspan's statement, the impulse response analysis documented in Section 4 does not necessarily point to a systematic monetary policy reaction to uncertainty directly. Second round effects, working through the impact that uncertainty shocks exerted on real activity and prices in our sample, represent an alternative, not mutually exclusive, potential explanation for the response of the policy rate. It is then of interest to shed further light on whether the Federal Reserve reacted directly to movements in uncertainty, acting as a ‘risk manager’, or rather it simply reacted to movements in real activity and prices induced by uncertainty shocks.

To isolate the direct systematic response of the Federal Reserve to variations in uncertainty, we proceed in two ways. First, we run a counterfactual simulation to produce the ‘risk management-driven policy rate gap’. This gap is constructed by computing the difference between the realised (i.e. historical) federal funds rate and the counterfactual policy rate that, according to our nonlinear VAR, we would have observed if the Federal Reserve had not systematically reacted to uncertainty in our sample. Specifically, we construct the counterfactual policy rate by only zeroing the coefficients on the uncertainty variable in the federal funds rate equation, and calculating its fitted values accordingly.^[19] Evidence of a negative gap would point to a higher interest rate in absence of a systematic policy response to uncertainty. Hence, it would be consistent with the claim that the Federal Reserve acted as a ‘risk manager’. Second, we analyse the minutes of the FOMC meetings to see whether there is narrative evidence in favour of risk management.

5.3.1 Empirical evidence

Figure 7 plots the difference between the historical and the counterfactual federal funds rate. Given that we consider all shocks hitting the economic system, the baseline scenario (the one that allows for the estimated systematic response of the federal funds rate to contemporaneous and past realisations of uncertainty) replicates the historical realisations of the federal funds rate. Two observations are in order. First, after the realisation of an uncertainty shock, the contemporaneous difference between the historical rate and the counterfactual one turns out to be negative. This suggests that, in absence of a systematic monetary policy response to uncertainty, the federal funds rate would have been higher in the aftermath of spikes in uncertainty. Second, the gap between the historical and the counterfactual policy rates widens in recessions by −47 basis points on average.

Our counterfactual exercise also points to a non-negligible positive effect of this risk management approach on industrial production. As documented in Table 1, the average deviation of the historical realisations of industrial production from the ‘no risk management’ path over the entire sample is 0.66 per cent. In other words, if the Federal Reserve did not consider macro uncertainty when setting monetary policy, the level of industrial production would have been lower. Looking at differences across the business cycle, this gap would have been larger in expansions. The indication of a lower push for industrial production by systematic monetary policy in recessions is in line with our previous finding that systematic monetary policy is less effective during economic downturns.

Differently, not much change would have emerged on average as regards employment and inflation. These macroeconomic aggregates would have followed a similar historical path regardless of risk management. Thus, the lower level of industrial production that we would have observed if the Federal Reserve did not act as a risk manager (i.e. if it did not keep interest rates low in response to heightened uncertainty) would likely be, in part, a result of lower capital stock in the economy.^[20]

5.3.2 Narrative evidence

Our empirical analysis assigns a role to uncertainty as a driver of US monetary policy decisions. We link this empirical evidence to the narrative evidence that emerges from the reading of the FOMC minutes. Specifically, we collect excerpts from the FOMC minutes (released around our uncertainty shock dates) with references to uncertainty, risk, and risk management (see Table C1). The reading of the FOMC minutes confirms that uncertainty was an element carefully considered by the members of the FOMC when deciding over the federal funds rate setting. We highlight some of the most informative examples below.

Uncertainties related to external events like the first oil crisis and the Arab-Israeli called for cautious behaviour at the end of 1973:

… in light of current uncertainties regarding the economic outlook and the sensitive state of financial market psychology, current money market conditions be maintained for the time being.

Black Monday is a textbook example of an uncertainty-inducing event. In October 1987, the minutes report that:

The Committee recognizes that still sensitive conditions in financial markets and uncertainties in the economic outlook may continue to call for a special degree of flexibility in open market operations.

The risk management approach by the Federal Reserve appears evident also during the Asian crisis, as the reading of the December 1997 minutes suggests:

While developments in Southeast Asia were not expected to have much effect on the U.S. economy, global financial markets had not yet settled down and further adverse developments could have greater-than-anticipated spillover effects on the ongoing expansion. In this environment, with markets still skittish, a tightening of U.S. monetary policy risked an oversized reaction … At the conclusion of the Committee's discussion, all but one member supported a directive that called for maintaining conditions in reserve markets that were consistent with an unchanged federal funds rate of about 5-1/2 percent and that retained a bias toward the possible firming of reserve conditions and a higher federal funds rate during the intermeeting period.

Another example is the Gulf War II. It created a high degree of uncertainty about future domestic economic outcomes, as suggested by the March 2003 minutes:

… members commented that an unusually high degree of uncertainty had made it very difficult to assess the factors underlying the performance of the economy … In light of these considerable uncertainties, the members agreed that heightened surveillance of evolving economic trends would be especially useful in the weeks ahead … the Committee in the immediate future seeks conditions in reserve markets consistent with maintaining the federal funds rate at an average of around 1-1/4 percent.

Wrapping up, both our econometric results and the narrative evidence based on the FOMC minutes point to a risk management approach by the Federal Reserve. In periods of expectations of sustained future growth and inflationary pressures surrounded by high uncertainty, this risk management practice translated into a ‘wait-and-see’ behaviour, that is, increases in the policy rate to tackle nascent inflation were postponed (e.g. the response to the 1997 uncertainty shock related to the Asian crisis). Differently, expectations of a gloomy economic scenario in a high uncertainty environment led the Federal Reserve to implement larger decreases of the policy rate than those that would have been implemented in absence of uncertainty (e.g. the decisions taken after the 1990 first Gulf War shock and after the 9/11 attacks).^[21]

Evans et al (2015) also identify excerpts of the FOMC minutes that discuss uncertainty triggered by national and international factors. They assess the statistical and economic relevance of risk management by the US monetary policymakers by constructing judgemental and automatic (keyword-based) indicators using the minutes of the FOMC meetings as a database. They then use these indicators, along with a number of other proxies for uncertainty, to estimate augmented Taylor rules in which these measures of uncertainty are included one at a time on top of inflation and output. Evans et al (2015) find evidence pointing to a significant and negative contemporaneous response of the Federal Reserve to uncertainty in the period 1987–2008. Hence, their evidence suggests that the Federal Reserve adopted a looser policy in the presence of uncertainty. Our analysis and narrative-based investigation point to the same qualitative conclusion.^[22]

Footnotes

The baseline and counterfactual scenarios produce similar responses for the first few months after the shock. This is due to the relevance of initial conditions, which are dominant during the first periods. In fact, initial conditions heavily influence the evolution of the transition indicator and, therefore, the probability of being in a recession. Systematic policy takes time before notably affecting the economic system and, consequently, the value of the logistic function in our STVAR. However, as periods go by, policy exerts an impact on the evolution of the economic system, above all in expansions. [17]

Controlling for fiscal policy in our STVAR would require us to work with a different sample as most fiscal aggregates are available only at a quarterly frequency. Data aggregation could lead to a loss of useful information, as it would reduce the number of observations available for estimation. Therefore, we leave the investigation of potentially nonlinear fiscal-monetary policy interactions for future research. [18]

This counterfactual differs from the one conducted in Section 5.1 in two ways. First, we constrain only the coefficients on uncertainty (and not on all the other variables as we did before). Second, instead of computing impulse responses to an uncertainty shock, we iterate the model forward to produce the alternative ‘no risk management’ path of the variables included in our analysis. [19]

We do not explore the counterfactual paths of wages and hours as our VAR model might not adequately capture labour market dynamics. These variables are included in the model mostly to help explain aggregate employment and industrial production. [20]

Interestingly, our evidence is also in line with the following recent statement by Janet Yellen (Chairman of the Federal Reserve):

The recovery from the Great Recession has advanced sufficiently far and domestic spending appears sufficiently robust that an argument can be made for a rise in interest rates at this time. We discussed this possibility at our meeting. However, in light of the heightened uncertainties abroad and a slightly softer expected path for inflation, the Committee judged it appropriate to wait for more evidence, including some further improvement in the labor market, to bolster its confidence that inflation will rise to 2 percent in the medium term (Yellen 2015).

[21]

To understand how quantitatively close our results are to Evans et al's (2015), we conduct the following exercise. We estimate their Taylor rule over the sample 1987:Q1–2008:Q2 by allowing for a nonlinear response of the policy rate in NBER recessions/expansions to uncertainty, which is proxied by the VXO. Then, we produce the ‘Taylor rule risk management-driven policy rate gap’ by taking the difference between the historical policy rate and the one produced by sticking to historical values of core inflation, the output gap, and (lagged realisations of) the policy rate, in a version of the Taylor rule conditional on a zero response to uncertainty. The resulting Taylor rule policy rate gap: i) displays large realisations (in absolute terms) in recessions, and ii) points to a value as large as 114 basis points in 2001:Q4. Details on the derivation of the Taylor rule rate gap are documented in Appendix C. [22]