RDP 2017-06: Uncertainty and Monetary Policy in Good and Bad Times 1. Introduction

Uncertainty shocks have recently been identified as one of the drivers of US business cycles (Bloom 2009; Bloom et al 2014; Jurado, Ludvigson and Ng 2015; Leduc and Liu 2016; Basu and Bundick 2017). This paper investigates the nonlinear effects of uncertainty shocks as well as the relationship between macroeconomic uncertainty and monetary policy in the United States. It does so by addressing three related questions: Are the effects of uncertainty shocks different in good and bad times? Is the stabilising power of systematic monetary policy in response to uncertainty shocks state-contingent? Do monetary policymakers respond to movements in uncertainty per se? We answer these questions by modelling a standard set of post-WWII US macroeconomic variables with a smooth transition vector autoregression (STVAR) model. This nonlinear framework allows us to capture the possibly different macroeconomic responses to an uncertainty shock occurring in different phases of the business cycle. We endogenously account for potential regime-switches due to an uncertainty shock by computing generalised impulse response functions (GIRFs) à la Koop, Pesaran and Potter (1996). Using GIRFs is important to correctly address the above mentioned questions because: i) uncertainty shocks that occur in expansions could drive the economy into a recessionary state, and ii) uncertainty shocks occurring in recessions may lead the economy to a temporary expansion in the medium term as uncertainty dissipates (Bloom 2009).

Our focus on nonlinearities is justified by two important stylised facts. First, most macroeconomic aggregates display an asymmetric behaviour over the business cycle (see, among others, Sichel (1993); Koop and Potter (1999); van Dijk, Teräsvirta and Franses (2002); Caggiano and Castelnuovo (2011); Morley and Piger (2012); Abadir, Caggiano and Talmain (2013); and Morley, Piger and Tien (2013)). Second, uncertainty features different dynamics in good and bad times. Micro- and macro-evidence of countercyclical uncertainty with abrupt increases in recessions is documented by Bloom (2009), Bloom et al (2014), Orlik and Veldkamp (2014), and Jurado et al (2015). Moreover, different indicators of realised volatility, often taken as a proxy for expected volatility in empirical analysis, are higher and more volatile in recessions (Bloom 2014, 2017).^[1] In light of this evidence, one might expect uncertainty shocks to have different macroeconomic effects over the business cycle. Theoretical support for this intuition is provided by Cacciatore and Ravenna (2015). They work with a model featuring matching frictions in the labour market and show that deviations from efficient wage-setting (due to such frictions), combined with downward wage rigidities, imply a state-dependent amplification of the real effects of uncertainty shocks, and contribute to make uncertainty countercyclical. Importantly, this set of assumptions is more realistic than theoretically frictionless labour markets. Empirical support for Cacciatore and Ravenna's conjecture is provided by Caggiano, Castelnuovo and Groshenny (2014), Nodari (2014), Ferrara and Guérin (2015), Casarin et al (2016), and Caggiano, Castelnuovo and Figueres (2017). Our investigation complements these others by unveiling the interactions between uncertainty shocks and systematic monetary policy in different phases of the US business cycle. We study the US economy for two reasons. First, there is a reasonably-established literature on the linear effects of uncertainty shocks in the United States. This allows us to focus on nonlinearities and take as given the negative economic effects in the linear case.^[2] Second, to better model nonlinearities related to economic states, we need a large number of observations and a long sample that includes enough recessions and expansions. The US data we use is available at a monthly frequency since the early 1960s. Applying our econometric model to other economies such as Australia would be more complex due to the shorter period of data availability as well as the low occurrence of recessions and the lack of monthly indicators relevant to our analysis, such as the CPI index.

Following Bloom (2009), the identification of uncertainty shocks pursued in this paper relies on extreme events, that is events associated with large jumps in the level of the S&P 100 Volatility Index (VXO). Such events are related to terror, war, oil and the economy, and are usually bad events; one example is the assassination of John F Kennedy. Thus, our uncertainty shocks can be defined as shocks to the volatility of the US stock market induced by ‘extreme bad events’. These events are likely to be informative as regards unexpected movements in uncertainty that are not associated with the business cycle. Hence, we see these events as valid instruments to overcome the endogeneity problem one faces when searching for exogenous variations in uncertainty. Our results are robust to the employment of the VXO itself as an indicator of uncertainty in our STVAR as well as to the construction of an alternative event dummy based on the financial uncertainty proxy constructed by Ludvigson, Ma and Ng (2015).

Our focus on financial proxies of uncertainty is justified both theoretically and empirically. From a theoretical standpoint, Basu and Bundick (2017) show that movements in a measure of financial uncertainty, which is conceptually in line with the VXO, can be an important driver of the business cycle in a micro-founded macroeconomic model. Empirically, recent findings by Ludvigson et al (2015) and Casarin et al (2016) point to movements in financial uncertainty as possibly exogenous to the business cycle, and able to explain a larger share of the forecast error variance of real activity than movements in real activity indicators of uncertainty.^[3]

Are the effects of uncertainty shocks different in good and bad times? We find compelling evidence in favour of a positive answer. Real activity, measured by industrial production and employment, falls much more quickly and sharply when uncertainty shocks hit the economy during recessions. In regards to nominal variables, uncertainty shocks are deflationary, especially in recessions. The response of the policy rate is substantially more marked during economic downturns. Importantly, the difference in the estimated responses in the two states – recessions and expansions – is statistically significant as regards real activity and the policy rate.

Next, we investigate whether the effectiveness of systematic US monetary policy is state-dependent. The term ‘systematic’ here refers to the endogenous movements in the federal funds rate in response to macroeconomic conditions in the aftermath of uncertainty shocks. We run a counterfactual exercise in which systematic policy is assumed not to react to uncertainty as well as to the macroeconomic fluctuations triggered by uncertainty shocks. In other words, we shut down the direct and indirect effects of uncertainty shocks on the federal funds rate. We find a greater effectiveness of policy in tackling uncertainty shocks during expansions. In bad times, the depth of the economic downturn (following an uncertainty shock) remains virtually unchanged, while its persistence is only mildly influenced by the policy rate response to the shock. Differently, in expansions the absence of a systematic policy response would induce a much deeper and longer-lasting downturn after an uncertainty shock. Thus, monetary policy plays an important role in reducing the probability of entering a recession if the uncertainty shock occurs in good times. This is because, in good times, the expansionary policy response mitigates the drop in real activity. But it doesn't help as much if the economy is already in a recessionary state.

Finally, we dig deeper into the systematic relationship between uncertainty and monetary policy in the United States by running a second counterfactual simulation. Specifically, we shut down only the direct effects of uncertainty shocks on the federal funds rate, while allowing monetary policy to respond to all the remaining variables in the system. This is done to understand to what extent the Federal Reserve acted, borrowing the terminology proposed by Greenspan (2004), as a ‘risk manager’ and set the nominal interest rate lower than what it would have set in the absence of uncertainty. The counterfactual policy rate is systematically higher than the historical one in the aftermath of abrupt increases in uncertainty. The gap between the historical federal funds rate and the counterfactual one, which we term ‘risk management-driven policy rate gap’, confirms that risk management was a crucial element of US monetary policy decisions during the period 1962–2008. Importantly, and in line with our previous findings, the risk management-driven policy rate gap tends to be larger in recessions. We show that, absent this risk management policy, we would have observed a lower level of industrial production in the post-WWII period. We corroborate this finding by providing narrative evidence based on our reading of the Federal Open Market Committee (FOMC) minutes released around the uncertainty shocks we identify.

Our evidence on the risk management approach followed by the Federal Reserve is consistent with the results put forth by Evans et al (2015). They estimate several Taylor rules and find evidence in favour of a systematic response of the federal funds rate to a number of uncertainty indicators. In this regard, the key difference between their study and ours is that we run counterfactual simulations conducted with a multivariate nonlinear VAR framework. Our approach allows us to account for second round effects involving the policy rate, uncertainty, and measures of real economic activity.

From a modelling standpoint, our results support the development and use of nonlinear models able to replicate both the contractionary effects and the different economic transmission of uncertainty shocks over the business cycle. Policy wise, our findings offer support for research investigating how to efficiently tackle the state-dependent effects of such shocks.

The paper develops as follows. Section 2 discusses connections with the existing literature. Section 3 presents our nonlinear framework and the data employed in the empirical analysis. Section 4 documents the nonlinear effects of uncertainty shocks and discusses a number of robustness checks. Section 5 analyses the role of systematic monetary policy in recessions and expansions, quantifies to which extent uncertainty systematically affects the policy rate setting, and offers narrative evidence in favour of risk management by the Federal Reserve. Section 6 concludes.

Footnotes

Spikes in uncertainty indicators occur also in good times. For instance, the volatility index we use registered a substantial increment after Black Monday (19 October 1987), during a period classified as expansionary by the NBER. In general, however, increases in uncertainty during bad times are much more abrupt than those occurring in good times. [1]

We show the linear effects of uncertainty shocks, computed with our model, in Appendix B. [2]

Carriero, Clark and Marcellino (2016) model a large dataset of macroeconomic and financial variables and compute the effects of macroeconomic and financial uncertainty shocks. They find that macroeconomic uncertainty has a large and significant effect on real activity, but has a limited impact on financial variables. Differently, financial uncertainty has an impact on both financial and macroeconomic indicators. Given the inclusion of the S&P 500 index and measures of interest rates in our study, our focus on a financial-related uncertainty proxy is also intended to maximise the likelihood of capturing the real effects of uncertainty shocks via movements in financial markets. [3]