Forecasting macroeconomic tail risk in real time: Do textual data add value?

Abstract

We examine the incremental value of news-based data relative to the FRED-MD economic indicators for quantile predictions of employment, output, inflation, and consumer sentiment in a high-dimensional setting. Our results suggest that news data contain valuable information not captured by a large set of economic indicators. We provide empirical evidence that this information can be exploited to improve tail risk predictions. The added value is largest when media coverage and sentiment are combined to compute text-based predictors. Methods that capture quantile-specific non-linearities produce superior forecasts to those with linear predictive relationships. The results are robust along different modeling choices.