MONETARY POLICY UNCERTAINTY AND ECONOMIC FLUCTUATIONS

• Author: Drew D. Creal,Jing Cynthia Wu
• Published date: 01 November 2017
• Date: 01 November 2017
• DOI: http://doi.org/10.1111/iere.12253

INTERNATIONAL ECONOMIC REVIEW

Vol. 58, No. 4, November 2017

MONETARY POLICY UNCERTAINTY AND ECONOMIC FLUCTUATIONS∗

BYDREW D. CREAL AND JING CYNTHIA WU1

University of Chicago, U.S.A

We investigate the relationship between uncertainty about monetary policy and its transmission mechanism,

and economic fluctuations. We propose a new term structure model where the second moments of macroeco-

nomic variables and yields can have a first-order effect on their dynamics. The data favor a model with two

unspanned volatility factors that capture uncertainty about monetary policy and the term premium. Uncertainty

contributes negatively to economic activity. Two dimensions of uncertainty react in opposite directions to a

shock to the real economy, and the response of inflation to uncertainty shocks varies across different historical

episodes.

1. INTRODUCTION

We investigate the relationship between uncertainty about monetary policy and its transmis-

sion mechanism and economic fluctuations. The core question of interest is: Does uncertainty

about monetary policy have a real effect? An equally important question is: How do macroe-

conomic shocks influence interest rate uncertainty? Although numerous studies have focused

on monetary policy and its transmission mechanism, less attention has been placed on under-

standing uncertainty surrounding this transmission mechanism and their relation with the real

economy. We study these questions by introducing a new term structure model with two novel

features. First, we jointly model the first and second moments of macroeconomic variables and

yields: Uncertainty is extracted from their volatility, and it has a direct impact on the conditional

means of these variables in a vector autoregression (VAR).2

Second, we decompose uncertainty of interest rates into two economic dimensions: the policy

component and the market transmission component captured by the term premium. Public

commentary by policymakers at central banks worldwide indicates that the term premium is

one of the most important pieces of information extracted from the term structure of interest

rates. Understanding the term premium and its uncertainty is crucial in making policy decisions

and evaluating how successful monetary policy is in achieving its goals.

We contribute to the term structure literature by devising a no-arbitrage model with multiple

unspanned stochastic volatility (USV) factors; i.e., the factors driving volatility are distinct from

the factors driving yields. We show that our model can successfully fit the data for both the cross

∗Manuscript received March 2015; revised June 2016.

1We thank Torben Andersen, Peter Christoffersen, Todd Clark, Steve Davis, Marty Eichenbaum, Bjorn Eraker,

Jesus Fernandez-Villaverde, Jim Hamilton, Lars Hansen, Steve Heston, Jim Nason, Giorgio Primiceri, Dale Rosen-

thal, Dora Xia, Lan Zhang, three anonymous referees, and seminar and conference participants at Chicago Booth,

Northwestern, UCL, Ohio State, U of Washington, NC State, Cleveland Fed, Illinois, Indiana, Texas A&M, Houston,

Bank of England, Bank of Japan, Deutsche Bundesbank, Conference in Honor of James Hamilton, Annual Econo-

metric Society Winter Meetings, ECB workshop on “New techniques and applications of Bayesian VARs,” Fifth Risk

Management Conference, UCSD alumnae conference, MFA, Midwest Econometrics, and CFE. Drew Creal gratefully

acknowledges financial support from the William Ladany Faculty Scholar Fund at the University of Chicago Booth

School of Business. Cynthia Wu gratefully acknowledges financial support from the IBM Faculty Research Fund at

the University of Chicago Booth School of Business. This article was formerly titled “Term Structure of Interest Rate

Volatility and Macroeconomic Uncertainty” and “Interest Rate Uncertainty and Economic Fluctuations.” Please ad-

dress correspondence to: Jing Cynthia Wu, The University of Chicago, Booth School of Business, 5807 South Woodlawn

Avenue, Chicago, IL 60637. E-mail: cynthia.wu@chicagobooth.edu.

2We define uncertainty as log volatility.

1317

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(2017) by the Economics Department of the University of Pennsylvania and the Osaka University Institute of Social

and Economic Research Association

1318 CREAL AND WU

section of yields and their volatility, and the data suggest two volatility factors. We introduce

a new rotation to the literature to capture the factor structure in an economically meaningful

way. We decompose the long-term interest rate into the expectation component and the term

premium component. The former is agents’ expectation about the future path of monetary

policy, which the central bank can influence through policies like forward guidance. The latter

relies on the market and captures how monetary policy gets propagated from the short-term

interest rate to long-term interest rates. The new rotation utilizes this decomposition and sets

the three yield factors to be the short-term interest rate, the expectation component of the long

rate, and the term premium component. The two volatility factors are for the short rate and

term premium, which can be conveniently interpreted as uncertainty about monetary policy

and its transmission mechanism.

We document the relationship between interest rate uncertainty and economic fluctuations

through impulse responses. Uncertainty is countercyclical and precedes worse economic condi-

tions and higher unemployment rates. This finding is consistent with the existing literature on

uncertainty. What sets this article apart from the literature is that our focus is on two aspects of

interest rate uncertainty. The distinction between the two dimensions lies in how they react to

news about the real economy. A higher unemployment rate leads to higher uncertainty about

term premia, which reflects the market’s reaction to bad economic news. In contrast, uncertainty

about monetary policy decreases in response to the same news. This is consistent with the Fed’s

proactive response to combat crisis historically.

One benefit of jointly modeling the first and second moments simultaneously is to allow

the impulse responses to vary through time depending on the state of the economy. This is

not possible for the models in the literature, unless they have time-varying autoregressive

coefficients. Empirically, the response of inflation to uncertainty shocks varies through time.

For example, in response to a positive shock to monetary policy uncertainty, inflation kept

increasing during the Great Inflation, when high inflation was considered bad for the economy.

In contrast, inflation decreased in response to the same shock during Volcker’s tenure. This

is consistent with Volcker’s reputation as an inflation hawk. It also barely responded during

the Great Recession, when the concern is centered around deflation. These demonstrate the

noncyclical feature of inflation. A positive shock to term premia uncertainty leads to a positive

reaction of inflation during Greenspan’s Conundrum and a negative reaction for the Volcker

period. The former adds additional evidence of the importance of the term premium during

that period. All of these economically meaningful distinctions can only be observed through

time-varying impulse responses. Standard impulse responses are close to zero, insignificant,

and potentially misleading because they are averages of the positive and negative time-varying

impulse responses.

Our historical decomposition further quantifies the two-way link between the real economy

and interest rate uncertainty. Historically monetary policy uncertainty has contributed

negatively to the inflation rate, which heightened at −0.7% after the Great Recession,

placing further deflationary pressure during that period. Both monetary policy uncertainty

and term premium uncertainty added positively to the unemployment rate historically. The

contribution of monetary policy uncertainty peaked in the early 1980s at about 0.55%,

whereas that of term premium uncertainty had peaks in the early 1970s, early 1980s, and

mid-2000s at the highest of 0.7%. The peak in the 2000s is associated with Greenspan’s

Conundrum, where our empirical evidence is consistent with the popular view that the term

premium and its uncertainty increased. How monetary policy uncertainty and term premium

uncertainty factor into unemployment differs since the Great Recession, with the former

becoming negative potentially due to less uncertainty surrounding monetary policy and the

latter remaining positive with still significant uncertainty in the market. Consistent with

our impulse responses, inflation contributed positively to both uncertainty measures in the

1980s and negatively at the beginning and end of our sample period. The contributions of

unemployment rate shocks to monetary policy uncertainty and term premium uncertainty take

opposite signs. This is further evidence of the two dimensions of uncertainty.

MONETARY POLICY UNCERTAINTY AND ECONOMIC FLUCTUATIONS 1319

Our article contributes to the econometrics literature on estimation of VARs with stochastic

volatility. When volatility enters the conditional mean of a VAR, the popular Markov chain

Monte Carlo (MCMC) algorithms for stochastic volatility models of Kim et al. (1998) cannot

be used. We develop an MCMC algorithm based on the particle Gibbs sampler that is efficient

and can handle a wide range of models. We are the first to introduce this algorithm into the

macrofinance literature.

The remainder of the article is organized as follows: We describe our relationship to the liter-

atures in Subsection 1.1. Section 2 presents the new term structure model together with the new

rotation. Section 3 describes the MCMC and particle filtering algorithms used for estimation. In

Section 4, we study the economic implications of interest rate uncertainty. Section 5 demon-

strates how a collection of models with different specifications fit the yield curve. Section 6

concludes.

1.1. Related Literature. Our article is closely related to recent advances in the literatures

on uncertainty and the term structure of interest rates. First, our article contributes to the

fast-growing literature on the role that uncertainty shocks play in macroeconomic fluctuations,

asset prices, and monetary policy; see, e.g., Bloom (2014) for a survey; Baker et al. (2015),

Jurado et al. (2015), Bekaert et al. (2013), and Aastveit et al. (2013) for empirical evidence;

and Ulrich (2012), P´

astor and Veronesi (2012), and P´

astor and Veronesi (2013) for theoretical

models.

We differ from the empirical papers in the uncertainty literature in the following ways. (i)

We internalize the uncertainty: In our model, uncertainty serves both as the second moment of

macroeconomic variables and yields (the factors driving the volatility of inflation, unemploy-

ment, and interest rates), and it directly impacts the first moment of macroeconomic variables.

In contrast, the uncertainty literature typically extracts an estimate of uncertainty in a data

preprocessing step, often as the second moment of observed macroeconomic or financial time

series. Researchers then use this estimate in a second step as an observable variable in a ho-

moskedastic VAR. (ii) This literature has so far focused on one dimension of uncertainty,

whether it is policy uncertainty or macroeconomic uncertainty. We discuss two dimensions

of interest rate uncertainty and their distinct economic implications. (iii) Different from the

rest of the literature, we focus on uncertainty about monetary policy and its transmission

mechanism.

Our article is also related to the VAR literature with stochastic volatility; see Cogley and

Sargent (2001, 2005), and Primiceri (2005) for examples. We adopt a similar approach to

modeling the time-varying covariance matrix as Primiceri (2005). Although they use a different

modeling approach, Fern´

andez-Villaverde et al. (2011) also study the real effect of volatility

on the (real) interest rate in an open emerging economy setting. A recent paper by Mumtaz

and Zanetti (2013) is closely related to ours in terms of how we specify the factor dynamics.

Both papers allow the volatility factors to enter the conditional mean and have a first-order

impact on key macroeconomic aggregates. This is absent from most of the existing models in

this literature, and we show its importance through impulse responses. The main difference

between our article and Mumtaz and Zanetti (2013) is that our article introduces a factor

structure for the volatilities and ties these factors into a no-arbitrage term structure model. Our

article also introduces a new and more efficient MCMC algorithm known as a particle Gibbs

sampler, Andrieu et al. (2010), which can be used for a wide variety of multivariate time-series

models.

Finally, we contribute to the term structure literature by introducing a flexible way to simul-

taneously fit yields and their volatilities at different maturities. In the earlier literature, e.g., Dai

and Singleton (2000) and Duffee (2002), volatility factors must simultaneously fit both the level

of yields and their volatility. The factors from estimated models end up fitting the conditional

mean of yields, and consequently they do not accurately estimate the conditional volatility. To

break the tension, Collin-Dufresne and Goldstein (2002) propose the class of USV models that