Working Paper: NBER ID: w26648
Authors: Leland Bybee; Bryan T. Kelly; Asaf Manela; Dacheng Xiu
Abstract: We propose an approach to measuring the state of the economy via textual analysis of business news. From the full text content of 800,000 Wall Street Journal articles for 1984{2017, we estimate a topic model that summarizes business news as easily interpretable topical themes and quantifies the proportion of news attention allocated to each theme at each point in time. We then use our news attention estimates as inputs into statistical models of numerical economic time series. We demonstrate that these text-based inputs accurately track a wide range of economic activity measures and that they have incremental forecasting power for macroeconomic outcomes, above and beyond standard numerical predictors. Finally, we use our model to retrieve the news-based narratives that underly “shocks” in numerical economic data.
Keywords: textual analysis; economic measurement; forecasting; news attention; macroeconomic outcomes
JEL Codes: C43; C55; C58; C82; E0; E17; E32; G0; G1
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| recession attention (E32) | industrial production (L69) |
| recession attention (E32) | employment (J68) |
| news attention (E60) | economic indicators (E01) |