The Role of Credit in Predicting US Recessions

• DOI: http://doi.org/10.1002/for.2448
• Author: Harri Ponka
• Published date: 01 August 2017
• Date: 01 August 2017

Journal of Forecasting,J. Forecast. 36, 469–482 (2017)

Published online 15 September 2016 in Wiley Online Library (wileyonlinelibrary.com)DOI: 10.1002/for.2448

The Role of Credit in Predicting US Recessions

HARRI PONKA

Department of Political and Economic Studies, University of Helsinki, Finland

ABSTRACT

Westudy the role of credit in forecasting US recession periods with probit models. We employboth classical recession

predictors and common factors based on a large panel of financial and macroeconomic variables as control variables.

Our findings suggest that a number of credit variables are useful predictors of US recessions over and above the

control variables both in and out of sample. In particular, the excess bond premium, capturing the cyclical changes

in the relationship between default risk and credit spreads, is found to be a powerful predictor of recession periods.

Copyright © 2016 John Wiley & Sons, Ltd.

KEY WORDS business cycle; credit spread; factor models; forecasting; probit models

INTRODUCTION

The role of credit in business cycle fluctuations and financial crises has been a widely covered topic after the most

recent financial crisis (see, for example, Schularick and Taylor1870, Jorda 2014). These papers focus on the historical

role of credit and study how credit cycles and business cycles have coincided. Schularick and Taylor (1870) examine

the behavior of financial, monetary and macroeconomic indicators in 14 countries with annual data starting in 1870,

and uncover a key finding that exuberant credit growth has a tendencyto precede financial crises. In a related vein, the

role of credit spreads in predicting real activity has also attracted the interest of researchers. Theoretical frameworks

on the relationship between credit spreads and economic activity have been presented by Bernanke et al. (1999)

and Philippon (2008), for example, both of which relate the widening of credit spreads to economic downturns.

Empirical studies have also evaluated this relationship, and found that credit spreads havesignificant predictive ability

on business cycle fluctuations (see, for example, Gilchrist and Zakrajsek 2012, Faust et al., 2013).

The purpose of this paper is to study the role of credit and credit spreads in predicting US recessions. Following

the previous research, we employ binary response models to predict the state of the business cycle (see, for exam-

ple, Estrella and Mishkin 1998, Kauppi and Saikkonen 2008, Nyberg 2010, Christiansen et al.,2014). The previous

literature on predicting recessions has identified a number of leading indicators for assessing the risk of economic

downturns, and the role of financial variables has been especially highlighted. In particular,the predictive power of the

term spread on recession periods has been studied in a number of studies since Estrella and Hardouvelis (1991), who

find that it has strong predictive power on future changes of real economic activity and recession periods in excess

of variables such as short-term interest rates and lagged real output. Further studies, such as Estrella and Mishkin

(1998), Nyberg (2010) and Ng (2012), have reaffirmed the findings concerning the term spread and also suggested

that stock returns are useful leading indicators of recession periods.

While previous studies have already considered some credit variables as predictors (see, for example, Ng 2012,

Saar and Yagil 2015), our aim is to provide a more comprehensive look at the role of credit in predicting US reces-

sions. We select our predictors based on previous studies on the relationship between credit and economic activity.

Following Schularick and Taylor (1870), we use different measures of bank credit that describe credit growth.1

Secondly, we employ credit spreads, such as the ‘GZ credit spread’, a corporate credit spread index introduced by

Gilchrist and Zakrajsek (2012), who find that it has considerable predictive power for business cycle fluctuations.

Finally, we follow Cole et al. (2008), who use bank stock returns as a measure of general conditions in the banking

sector and find that they are a significant predictor of future economic growth.

Methodologically, we follow the footsteps of Christiansen et al. (2014), who study the role of sentiment variables

in predicting US recessions using factor-augmented probit models (see also Chen et al., 2011, Bellégo and Ferrara

Correspondence to: Harri Ponka, Department of Political and Economic Studies, University of Helsinki, Finland. E-mail: harri.ponka@

helsinki.fi

1There are obvious similarities in our approach compared to that of Schularick and Taylor (1870), i.e. the focus on credit variables and the use

of binary response models. However, there are also some keydifferences. They use a panel model with annual data to predict financial crises for

14 countries, whereas we use monthly data and focus on US business cycle recession periods. Financial crises and recessions naturally coincide

in many cases but, as financial crises are even more uncommon events than recessions, focusing only on financial crises in a single country

study is not feasible. For instance, the dataset used by Schularick and Taylor (1870) contained only two financial crisis periods in the post-World

War II sample.

Copyright © 2016 John Wiley & Sons, Ltd