Forecasting Inflation Using Economic Narratives

Abstract

We use economic narratives to forecast inflation with a large news corpus and machine learning algorithms. The economic narratives from the full text content of over 880,000 Wall Street Journal articles are decomposed into multiple time series representing interpretable news topics, which are then used to predict inflation. The results indicate that narrative-based forecasts are more accurate than the benchmarks, especially during recession periods. Narrative-based forecasts perform better in long-run forecasting, and provide incremental predictive information even after controlling macroeconomic big data. In particular, information about inflation expectations and prices of specific goods embedded in narratives contributes to their predictive power. Overall, we provide a novel representation of economic narratives and document the important role of economic narratives in inflation forecasting.

Keywords:

• Economic Narratives
• Inflation Forecasting
• Textual Analysis
• Machine Learning

JEL classification:

• C55
• E32
• E37

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Additional information

Notes on contributors

Yongmiao Hong

Yongmiao Hong, [email protected], Center for Forecasting Science, Chinese Academy of Sciences, China; School of Economics and Management, and MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation, University of Chinese Academy of Sciences, China.

Fuwei Jiang

Fuwei Jiang, [email protected], School of Finance, Central University of Finance and Economics, China.

Lingchao Meng

Lingchao Meng, [email protected], China School of Banking and Finance, University of International Business and Economics, China.

Bowen Xue

Bowen Xue, [email protected], School of Economics and Management, University of Chinese Academy of Sciences, China.