Disentangling the Effects of Uncertainty, Monetary Policy and Leverage Shocks on the Economy*
| Published date | 01 October 2021 |
| Author | Cosmas Dery,Apostolos Serletis |
| Date | 01 October 2021 |
| DOI | http://doi.org/10.1111/obes.12437 |
Disentangling the Effects of Uncertainty, Monetary
Policy and Leverage Shocks on the Economy*
COSMAS DERY and APOSTOLOS SERLETIS
Department of Economics, University of Calgary, Calgary, Alberta T2N 1N4, Canada
(e-mail: Serletis@ucalgary.ca)
Abstract
In this paper, we assess the information content and predictive ability of various risk
and uncertainty measures in predicting various measures of real economic activity as
well as undertake a comparative analysis of the relative importance of uncertainty,
monetary policy and leverage shocks in the macroeconomic business cycle. We find
that the macroeconomic uncertainty index and the Chicago Fed national financial
conditions risk index have the strongest predictive relationship with economic
activities. Also, in the context of a Bayesian monetary structural vector autoregressive,
we use the penalty function approach to a sequential identification of uncertainty,
monetary policy and leverage shocks, and find that uncertainty shocks are a relatively
more important source of variations in the economy than traditional monetary policy
shocks. However, monetary policy shocks still outperform uncertainty shocks in
explaining inflation dynamics.
I. Introduction
How important are uncertainty shocks compared to monetary policy and leverage
shocks? High uncertainty, whether caused by political unrest, trade restrictions, or the
COVID-19 crisis, generally has a negative effect on the level of economic activity. For
example, increased uncertainty typically leads to declines in real GDP, consumption,
investment, employment, inflation and interest rates –see Bloom (2009), Mumtaz and
Zanetti (2013), Jurado, Ludvigson and Ng (2015), Fern´
andez-Villaverde et al. (2015),
Bloom et al. (2018), Davis (2019), and Caldara et al. (2016, 2020), among others. In
recent years, as Cascaldi-Garcia et al. (2020) put it, ‘researchers, policymakers, and
market participants have become increasingly focused on the effects of uncertainty and
risk on financial market and economic outcomes’. On the other hand, financial
intermediaries have also been shown to be more active players in the economy than
was previously assumed. In this paper, we examine the interaction between uncertainty
JEL Classification numbers:E32, E44, E52, E58.
*This paper is based on Chapter 3 of Cosmas Dery’s Ph.D. thesis at the University of Calgary. We thank the
Editor, Francesco Zanetti, and three anonymous referees for comments that greatly improved the paper. We also
thank the following members of Cosmas’s dissertation committee: David Walls and Atsuko Tanaka.
1029
©2021 The Department of Economics, University of Oxford and John Wiley & Sons Ltd
OXFORD BULLETIN OF ECONOMICS AND STATISTICS, 83, 5 (2021) 0305-9049
doi: 10.1111/obes.12437
and the traditional sources of macroeconomic instability –monetary policy and
leverage shocks –in the context of a Bayesian monetary structural vector
autoregressive (VAR) model using the penalty function approach to sequentially
identify multiple shocks. We find that uncertainty shocks explain a significant fraction
of economic fluctuations, but monetary policy shocks outperform uncertainty shocks in
explaining inflation dynamics.
Theoretical and empirical background. The information-based monetary
misperceptions model of Lucas (1972), developed initially by Friedman (1968) and
Phelps (1970), is one of the most celebrated business cycle models in the past 50
years. According to the model, in a rational expectations setting, economic agents have
incomplete information about prices in the economy, and monetary shocks are a
principal cause of business cycles. In recent years, however, most economists think
that monetary shocks are not the principal cause of business cycle fluctuations. For
example, Baumeister and Hamilton (2018) use a Bayesian structural VAR model,
based on the new Keynesian approach to macroeconomics, and show that monetary
policy shocks are relatively unimportant in explaining key macroeconomic variations
compared to demand and supply shocks. However, the new Keynesian approach to
macroeconomics ignores the financial intermediary sector. In this regard, as policy
rates around the world reached the zero lower bound in the aftermath of the global
financial crisis, banks and a number of market-based financial intermediaries have
attracted a great deal of attention, and there is now almost universal agreement that the
global financial crisis originated in the banking system. As Dery and Serletis (2020b)
recently put it, ‘financial firms issue leverage to acquire assets in excess of net worth,
and also issue deposit liabilities, which are included in measures of the money supply.
The question then is whether there is a useful role of leverage and the aggregate
quantity of money in monetary policy and business cycle analysis’.
Regarding leverage, Adrian and Shin (2010) argue that ‘the evidence points to
financial intermediaries adjusting their balance sheets actively, and doing so in such a
way that leverage is high during booms and low during busts’. In fact, Geanakoplos
(2012 p. 389) argues that ‘leverage can be more important to economic activity and
prices than interest rates, and more important to manage’. In this regard, Istiak and
Serletis (2017) investigate the macroeconomic effects of leverage and the
interdependence between monetary policy and leverage and conclude that the role
played by leverage shocks seems more important than interest rate policy. Regarding
the role of money, McCallum and Nelson (2011 p. 147) argue that ‘too much in the
reaction to problems in measuring money has taken the form of abandoning the
analysis of monetary aggregates, and too little has taken the form of more careful
efforts at improved measurement’. In this regard, Dery and Serletis (2020b)
complement and extend the Baumeister and Hamilton (2018) model by including
financial intermediary leverage and money supply measures. They show that monetary
policy shocks account for a non-trivial proportion of the variation in output when
Divisia monetary aggregates are included in traditional interest rate monetary policy
rules.
However, as Caldara et al. (2016 p. 185) put it, ‘the acute turmoil that swept
through global financial markets during the 2008–2009 financial crisis and the depth
©2021 The Department of Economics, University of Oxford and John Wiley & Sons Ltd
1030 Bulletin
and duration of the associated economic downturn, both in the United States and
abroad, have cast a considerable doubt on the traditional sources of business cycle
fluctuations. In response, recent theoretical and empirical research aimed at
understanding these extraordinary events has pointed to financial and uncertainty
shocks –or their combination –as alternative drivers of economic fluctuations’.
Moreover, Davis (2019 p. 13) argues that ‘a variety of studies find evidence that high
(policy) uncertainty undermines economic performance by leading firms to delay or
forego investments and hiring, by slowing productivity-enhancing factor reallocation,
and by depressing consumption expenditures. This evidence points to a positive payoff
in the form of stronger macroeconomic performance if policymakers can deliver
greater predictability in the policy environment’.
Contribution. In this paper, in the spirit of Adrian and Shin (2010), Caldara et al.
(2016), Baumeister and Hamilton (2018), and Dery and Serletis (2020a) we assess the
relative importance of uncertainty, monetary policy and leverage shocks as sources of
macroeconomic fluctuations. In doing so, we ignore real shocks, fiscal shocks and
other shocks (such as oil shocks), leaving their investigation for future work –see
Cochrane (1994) and Ramey (2016) for a discussion of the shocks that drive economic
fluctuations. We follow Caldara et al. (2016) and use the penalty function approach to
a sequential identification of multiple shocks in the context of a Bayesian monetary
structural VAR framework. This approach, initially developed by Faust (1998) and
Uhlig (2005), and further extended by Mountford and Uhlig (2009), selects a structural
VAR model by maximizing a criterion function subject to inequality constraints, with
the criterion function consisting of the sum of the impulse response functions of the
target variable(s) and the inequality constraints corresponding to sign restrictions on
these impulse response functions over a pre-specified horizon. As Caldara et al. (2016
p. 186) argue, ‘compared with identification schemes based on sign restrictions, this
framework allows us to distinguish empirically between shocks that have otherwise
very similar qualitative effects on the economy’.
We find that most measures of risk and uncertainty are informative for predicting
real economic activities and produce statistically and economically significant
forecasts of economic activities. Based on the information content analysis of the
various risk and uncertainty measures, we conclude that macroeconomic uncertainty
and the Chicago Fed national financial conditions risk index are the most informative
for predicting real economic activity. Based on our structural VAR analysis, we find
that uncertainty shocks are more important in explaining fluctuations in the real
sector of the economy than monetary policy and leverage shocks. We find that a
positive uncertainty shock results in declines in the growth rate of real GDP,
investment, consumption and employment, and increases in the unemployment rate.
The contraction in the real economy from an adverse uncertainty shock is more
pronounced and persistent relative to a contractionary monetary policy shock. In
terms of forecast error variance decomposition, uncertainty shocks account for an
overwhelming proportion of the variation of all real variables that we consider,
ranging from 38% to 61% over a 3-year average, depending on the variable, while
the corresponding proportion for monetary policy shocks ranges from 3% to 10%.
However, monetary policy shocks outperform uncertainty shocks in explaining
©2021 The Department of Economics, University of Oxford and John Wiley & Sons Ltd
Uncertainty, monetary policy and leverage1031
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