Mood Swings and Business Cycles: Evidence from Sign Restrictions

• Published date: 01 September 2019
• Author: DEOKWOO NAM,JIAN WANG
• Date: 01 September 2019
• DOI: http://doi.org/10.1111/jmcb.12568

DOI: 10.1111/jmcb.12568

DEOKWOO NAM

JIAN WANG

Mood Swings and Business Cycles: Evidence from

Sign Restrictions

This paper provides new evidencethat bouts of optimism and pessimism are

an important source of U.S. business cycles,using the identification schemes

based on sign restrictions. We document that identified optimism and pes-

simism shocks account for about 30% of U.S. business-cycle fluctuations

in hours and output. In addition, our empirical findings are consistent with

the intensive- and extensive-margin adjustments in the U.S. labor market

over business cycles,providing further support to optimism shocks being an

important source of U.S. business cycles. The identified optimism shocks

are at least partially rational as total factor productivity is found to rise 8–12

quarters after an initial bout of optimism. While this later finding is con-

sistent with some previous findings in the news shock literature, we cannot

rule out that such episodes reflect self-fulfilling beliefs.

JEL codes: E1, E3

Keywords: optimism shocks, business cycle fluctuations, sign restrictions.

THERE IS A LONG TRADITION in macroeconomics suggesting

that business cycles may be primarily driven by bouts of optimism and pessimism.

Keynes’ well-known “animal spirits” comment is one expressionof this view. Within

this tradition, however,there is considerable disagreement with respect to the sources

of such changes in sentiment. At one extreme, there is the view that such mood

This paper is a substantial revision and replacement of the paper “Do Mood Swings Drive Business

Cycles and is it Rational?” by Paul Beaudry, Deokwoo Nam, and Jian Wang (2011). We thank the editor,

three anonymous referees, Paul Beaudry, Fabrice Collard, Andre Kurmann, Guido Lorenzoni, Barbara

Rossi, Frank Portier, Eric Sims, Henry Siu, Harald Uhlig, YongsungChang, and participants at the AEA

Annual Meeting, Barcelona GSE Summer Workshop, and various other conferences and seminars for

their insightful comments. We would also like to thank Jonas Arias, Juan F. Rubio-Ram´

ırez, and Daniel

F. Waggoner for many helpful discussions and sharing their Matlab codes, and thank Mario Forni, Luca

Gambetti, and Luca Sala for kindly sharing their estimates of the principal components of the U.S.

macroeconomic data. Deokwoo acknowledges support for this work from Hanyang University through a

general research fund (HY-2016).

DEOKWOO NAM is at Department of Economics and Finance, Hanyang University, (E-mail: deokw-

nam@hanyang.ac.kr). JIAN WANG is at Schoolof Management and Economics, CUHK Business School,

The Chinese University of Hong Kong, Shenzhen, China (E-mail: jianwang@cuhk.edu.cn).

Received September 14, 2016; and accepted in revised form August 21, 2018.

Journal of Money, Credit and Banking, Vol. 51, No. 6 (September 2019)

C

2018 The Ohio State University

1624 :MONEY,CREDIT AND BANKING

swings are entirely rational because of a self-fulfilling feedback loop. According

to this perspective, optimism causes an increase in economic activity that precisely

validates the original optimistic sentiment.1Closely related to this view, because of

its shared rational basis, is the news view of mood swings. In this view, optimism

arises when agents learn about forces that will positively affect future fundamentals,

so bouts of optimism precede positive changes in fundamentals but do not cause

them.2Finally, there is a third view suggesting that macroeconomic mood swings are

only driven by psychological factors and therefore are not directly related to future

developments of fundamentals.3

Although there has been considerable empirical research on the roles of beliefs,

news, and animal spirits in business-cycle fluctuations, there remains considerable

disagreement about the results. For example, regarding the importance of news

shocks, Barsky and Sims (2011, 2012) arrive at substantial different conclusions

from those of Beaudry and Portier (2006). The aim of this paper is to contribute

to the above debate regarding the source and nature of business cycles. We provide

new evidence on the relevance of optimism and pessimism as an important driver of

macroeconomic fluctuations by taking a sign restrictions approach to isolate innova-

tions in optimism in structural vector autoregression (SVAR) models. In particular,we

implement the theory and numerical algorithms for sign restrictions that are recently

developed by Arias, Rubio-Ram´

ırez, and Waggoner (2018).4

Our identification strategy imposes sign and zero restrictions on the impulse re-

sponses of some macrovariables in VAR models to identify what we refer to as

optimism shocks. The idea of our strategy is to isolate innovations in optimism that

are associated with booms in the stock market and private consumption. At the same

time, such optimism-related booms are orthogonal to current improvements in tech-

nology and are not associated with either expansionary monetary policy or upward

pressure on inflation. The noninflationary feature of optimism-driven booms is well

documented in the literature, for example, Christiano et al. (2008, 2010) and Beaudry

and Portier (2013).

Accordingly, we impose positivesign restrictions on the impact responses of stock

prices and consumption and the zero restriction on the impact response of total factor

productivity (TFP). These restrictions identify optimism shocks that are associated

with an immediate boom in the stock market and consumption, while such a boom is

not related to any current improvement in TFP. In addition, we impose the positive

sign restriction on the impact response of the (ex post) real interest rate, which is

measured by the policy interest rate minus the inflation rate. This restriction helps

1. For example, see Benhabib and Farmer (1994), Benhabib, Wang,and Wen (2015), Farmer and Guo

(1994), and Gunn and Johri (2013) among others.

2. For example, see Beaudry and Portier (2004, 2006), Cochrane (1994a, 1994b), Jaimovich and

Rebelo (2009), and Schmitt-Grohe and Uribe (2012) among others. Along this line, Arezki, Ramey, and

Sheng (2017) study the effects of news shocks on current account and other macrovariablesby using giant

oil field discoveries as news to increases in future output.

3. For example, see the book by Akerlof and Shiller (2009).

4. The theory and numerical algorithms of Arias, Rubio-Ram´

ırez, and Waggoner (2018) correct the

problems associated with the penalty function approach of Mountford and Uhlig (2009).