Modeling the relation between the US real economy and the corporate bond‐yield spread in Bayesian VARs with non‐Gaussian innovations

In this paper, we analyze how skewness and heavy tails affect the estimated relationship between the real economy and the corporate bond‐yield spread—a popular predictor of real activity. We use quarterly US data to estimate Bayesian VAR models with stochastic volatility and various distributional assumptions regarding the innovations. In‐sample, we find that—after controlling for stochastic volatility—innovations in GDP growth can be well described by a Gaussian distribution. In contrast, the yield spread appears to benefit from being modeled using non‐Gaussian innovations. When it comes to real‐time forecasting performance, we find that the yield spread is a relevant predictor of GDP growth at the one‐quarter horizon. Having controlled for stochastic volatility, gains in terms of forecasting performance from flexibly modeling the innovations appear to be limited and are mostly found for the yield spread.