The Inverted Curve and Recession: A Hoax, When It Ends?
Yosef Bonaparte (University of Colorado at Denver)
June 17, 2019
The paper shows that the chance inverted curve predicts recession is less than 3.9%, and even not statistically significant. But then we ask why investors still see linkage between inverted curve and recession? The behavior psychology research demonstrates that, for the majority, bad events (such as the 2007 event) register stronger and longer than good events, and vivid in investors’ memory. Finally, we show that the strongest and best predictor for recession is the current GDP growth.
Media and Business-Cycle Predictability
Salim Baz (Imperial College Business School), et al.
April 25, 2019
We construct empirical measures of U.S. business-cycle activity based on media mentions of the word “recession.” The measures differ by the chosen weighting scheme (simple, count weighted, or sentiment weighted). We show that the measures relying on specialized newspapers (SN), like the Financial Times, sharply rise during NBER recessions, whereas measures based on nonspecialized newspapers (NSN) provide a more noisy measure of the economy’s state. The different SN measures are useful predictors of U.S. business cycles, both in-sample and out-of-sample. Moreover, the measures favourably compare with existing business-cycle predictors, like the term premium and the default spread.
Time-Series Variation in Factor Premia: The Influence of the Business Cycle
Christopher Polk (London School of Economics), et al.
April 24, 2019
Factor cyclicality can be understood in the context of factor sensitivity to aggregate cash-flow news. Factors exhibit different sensitivities to macroeconomic risk, and this heterogeneity can be exploited to motivate dynamic rotation strategies among five commonly established factors: size, value, quality, low volatility and momentum. A timely and realistic identification of business cycle regimes, using leading economic indicators and global risk appetite, can be used to construct long-only factor rotation strategies with information ratios nearly twice as large as static multifactor strategies. Results are statistically and economically significant after accounting for transaction costs, capacity and turnover.
Surveying Business Uncertainty
David Altig (Federal Reserve Banks), et al.
May 27, 2019
We develop a new monthly panel survey of business executives and a new question design that elicits subjective probability distributions over own-firm outcomes at a one-year look- ahead horizon. Our Survey of Business Uncertainty (SBU) began in 2014 and now covers 1,500 firms drawn from all 50 states, every major industry in the nonfarm private sector, and a full range of firm sizes. We use SBU data to measure expected future outcomes for the growth of sales, employment, and investment for each firm and the uncertainty surrounding those expectations. Mean expectations are highly predictive of realized growth rates in the firm-level data, and subjective uncertainty is highly predictive of absolute forecast errors. We also use the SBU data to produce a Business Expectations Index (first moment) and a Business Uncertainty Index (second moment) for the U.S. economy. In Granger causality tests, the Business Expectations Index has statistically significant predictive power for a range of prominent business cycle indicators. The SBU also includes special questions that elicit additional information, including the perceived effects of specific government policy developments on the firm’s decisions and outcomes.
What Do Sectoral Dynamics Tell Us About the Origins of Business Cycles?
Christian Matthes and Felipe Schwartzman (Federal Reserve Bank of Richmond)
March 29, 2019
We use economic theory to rank the impact of structural shocks across sectors. This ranking helps us to identify the origins of U.S. business cycles. To do this, we introduce a Hierarchical Vector Auto-Regressive model, encompassing aggregate and sectoral variables. We find that shocks whose impact originate in the “demand” side (monetary, household, and government consumption) account for 43 percent more of the variance of U.S. GDP growth at business cycle frequencies than identified shocks originating in the “supply” side (technology and energy). Furthermore, corporate financial shocks, which theory suggests propagate to large extent through demand channels, account for an amount of the variance equal to an additional 82 percent of the fraction explained by these supply shocks.
Tong Zhang (University of Zurich)
April 16, 2019
This paper contributes to the literature on the effect of financial frictions on business cycle activity. We follow the “leverage cycles” approach in the spirit of Geanakoplos (2010) which argues that equilibrium fluctuations in collateral rates (equivalently haircuts, margins, or leverage), rather than just in interest rates, are a key driver of persistent fluctuations in economic activity. In particular, we focus on how adverse economic shocks can be amplified and prolonged by endogenous variations in haircuts in the standard macrofinance framework à la Kiyotaki and Moore (1997). In our model, collateral constraints are motivated by no-recourse loans, and the interest rate and the haircut are jointly determined as general equilibrium objects. We highlight the difference between the risk and the illiquidity of the collateral in determining the credit market equilibrium: an increase in risk increases both the interest rate and the haircut, while an increase in illiquidity increases the haircut but decreases the interest rate. Compared with the previous literature, our model allows us to decompose the transmission of adverse shocks through the credit market into the interest rate channel and the haircut channel, and evaluate their relative importance. The numerical exercises illustrate that risk shocks can generate sizable business cycle fluctuations through the credit market, and the haircut channel is dominant in times of low market liquidity.
Duration Dependence, Monetary Policy Asymmetries, and the Business Cycle
Travis J. Berge and Damjan Pfajfar (Federal Reserve)
March 25, 2019
We produce business cycle chronologies for U.S. states and evaluate the factors that change the probability of moving from one phase to another. We find strong evidence for positive duration dependence in all business cycle phases but find that the effect is modest relative to other state- and national-level factors. Monetary policy shocks also have a strong influence on the transition probabilities in a highly asymmetric way. The effect of policy shocks depends on the current state of the cycle as well as the sign and size of the shock.
Business Cycles Across Space and Time
Neville Francis (University of North Carolina), et al.
January 22, 2019
We study the comovement of international business cycles in a time series clustering model with regime-switching. We extend the framework of Hamilton and Owyang (2012) to include time-varying transition probabilities to determine what drives similarities in business cycle turning points. We find four groups, or “clusters”, of countries which experience idiosyncratic recessions relative to the global cycle. Additionally, we find the primary indicators of international recessions to be fluctuations in equity markets and geopolitical uncertainty. In out-of-sample forecasting exercises, we find that our model is an improvement over standard benchmark models for forecasting both aggregate output growth and country-level recessions.