Modeling and Forecasting Macroeconomic Downside Risk

Abstract

We document a substantial increase in downside risk to US economic growth over the last 30 years. By modelling secular trends and cyclical changes of the predictive density of GDP growth, we find an accelerating decline in the skewness of the conditional distributions, with significant, procyclical variations. Decreasing trend-skewness, which turned negative in the aftermath of the Great Recession, is associated with the long-run growth slowdown started in the early 2000s. Short-run skewness fluctuations imply negatively skewed predictive densities ahead of and during recessions, often anticipated by deteriorating financial conditions, while positively skewed distributions characterize expansions. The model delivers competitive out-of-sample (point, density and tail) forecasts, improving upon standard benchmarks, due to the strong signals of increasing downside risk provided by current financial conditions.

Capturing GDP nowcast uncertainty in real time

“…The features of GDP growth's distribution when conditioning on macroeconomic and financial variables are also likely to vary over the economic cycle. By conditioning on financial variables, Adrian et al (2019) and Delle-Monache et al (2020) notably find increased negative skewness accompanying a rise in volatility in the predictive distribution of US GDP growth in times of recessions. In normal times, however, GDP growth is close to being conditionally normally distributed.…”

“…They have been applied successfully to economic forecasting problems notably by Delle-Monache and Petrella (2017), Creal et al (2014) and Gorgi et al (2019). More recently, Delle-Monache et al (2020) use score driven methods to study macroeconomic downside risk by estimating time-varying location, scale and shape parameters. They use a large number of regressors related to financial conditions in a univariate setting.…”

Tail Forecasting with Multivariate Bayesian Additive Regression Trees

“…Two recent events, the financial crisis and the COVID-19 pandemic, have increased the interest in tail risks in macroeconomic outcomes. A fast-growing literature has focused on the risks of significant declines in GDP, with quantile regression as the main method to estimate tail risks (see, e.g., Adrian, Boyarchenko, and Giannone (2019); Adrian, et al (2018); Cook and Doh (2019); De Nicolò and Lucchetta (2017) ;Ferrara, Mogliani, and Sahuc (2019); Giglio, Kelly, and Pruitt (2016); González-Rivera, Maldonado, and Ruiz (2019); Delle Monache, De Polis, and Petrella (2020); Plagborg-Møller, et al (2020); Reichlin, Ricco, and Hasenzagl (2020); and Mitchell, Poon, and Mazzi (forthcoming)). For output growth, forecasting tail risks has some precedent in the literature on forecasting recessions or just periods of negative growth (see, e.g., Aastveit, Ravazzolo, and van Dijk (2018)).…”

Investigating Growth at Risk Using a Multi-country Non-parametric Quantile Factor Model

“…A recent influential paper is Adrian, Boyarchenko, and Giannone (ABG, 2019), hereafter ABG, which investigated the impact of financial conditions on the conditional distribution of GDP growth and found it to be important in the lower quantiles. Both prior and subsequent to ABG, a large literature has emerged using quantile regression methods to forecast tail risks to economic growth (see, among many others, Adrian, et al (2018); Cook and Doh (2019); De Nicolò and Lucchetta (2017); Ferrara, Mogliani, and Sahuc (2019); Giglio, Kelly, and Pruitt (2016); González-Rivera, Maldonado, and Ruiz (2019); Delle Monache, De Polis, and Petrella (2020); Plagborg-Møller, et al (2020); Reichlin, Ricco, and Hasenzagl (2020); Figueres and Jarociński (2020); and Mitchell, Poon, and Mazzi (forthcoming)). Other studies consider tail risks to other macroeconomic variables such as unemployment or inflation (e.g.…”