Understanding Growth-at-Risk: A Markov Switching Approach

Uncertainty before and during COVID‐19: A survey

“…A paper offering a unifying framework to study tail risks, first‐moment shocks, and uncertainty is Caldara et al. ( 2021a ). The link between nonlinear effects of uncertainty shocks and growth‐at‐risk appears to be a promising research avenue.…”

Specification Choices in Quantile Regression for Empirical Macroeconomics

“…To have a relatively large model in this case, we pull in indicators from a range of studies, some that have considered tail risks to economic activity measures other than GDP growth. More specifically, in the large GDP growth application, our choice of broad indicators is informed by the results of applications in Caldara, et al (2021) and Plagborg-Moller, et al (2020), who find that broad factor indexes of economic and financial conditions have predictive content for growth-at-risk; in a range of studies in the forecasting literature that generally find credit spreads to be helpful for macroeconomic forecasting (e.g., Faust, et al (2013)); and in Kiley (2022), who finds that a credit spread and medium-term changes in the credit-to-GDP ratio have predictive content for tail risks to economic activity as measured by the unemployment rate. Drawing on specifications from these studies, the broad set of indicators used for tail risk prediction in this large GDP growth application consists of the four NFCI subindexes for leverage, non-financial leverage, credit, and risk; the Baa corporate/10-year Treasury bond spread; the Aaa corporate/10-year Treasury bond spread; the four-year growth rate of the credit-to-nominal GDP ratio; and the Chicago Fed's national activity index (CFNAI) of the business cycle.…”

“…Accordingly, in this paper we examine various choices in the specification of quantile regressions used for modeling and forecasting macroeconomic tail risks. Our analysis is based on applications to US data on GDP growth, the unemployment rate, and inflation, patterned on empirical work by Adrian, Boyarchenko, and Giannone (2019), Caldara, et al (2021), and Plagborg-Moller, et al (2020) for output growth; Kiley (2022) for unemployment; and Korobilis, et al (2021) and Lopez-Salido and Loria (2022) for inflation. For each application, we compare the accuracy of quantile forecasts obtained from simple quantile regression, averages of quantile regression forecasts obtained with one indicator at a time, partial quantile regression, quantile regression with ridge penalty, and a few different specifications of priors for Bayesian quantile regression.…”

Constructing Density Forecasts from Quantile Regressions: Multimodality in Macro-Financial Dynamics

“…A large literature, of course, considers the production of density forecasts using other methods; see Aastveit et al (2019) for a review. A literature has also grown up, in response to ABG, on the production of GaR and density forecasts using both parametric and nonparametric alternatives to QR; for example, see Carriero et al (2020a), Caldara et al (2021), Plagborg-Moller et al (2020), De Polis et al (2020), and Adrian et al (2021). By contrast, we deliberately stick to the QR models of ABG.…”

“…challenge this view, noting that ABG's empirical finding that downside risk varies more than upside risk could equally well be explained by symmetric conditional forecast densities as by asymmetric unconditional forecast densities. These could be produced, for example, by Bayesian VAR models with stochastic volatility Caldara et al (2021). similarly suggest use of a parametric modeling framework that rationalizes the empirical findings of ABG but maintains use of symmetric conditional densities.…”