Text as data: a machine learning-based approach to measuring uncertainty

TL;DR

This paper introduces a machine learning-based method to measure economic uncertainty using news feed data, demonstrating its effectiveness in capturing uncertainty trends in the US from 1996 to 2020.

Contribution

It presents a novel approach to quantify economic uncertainty through news data and machine learning, providing a new tool for economic analysis.

Findings

The new measure Granger-causes the EPU index.

The series shows no reverse Granger causality.

The method effectively captures uncertainty dynamics.

Abstract

The Economic Policy Uncertainty index had gained considerable traction with both academics and policy practitioners. Here, we analyse news feed data to construct a simple, general measure of uncertainty in the United States using a highly cited machine learning methodology. Over the period January 1996 through May 2020, we show that the series unequivocally Granger-causes the EPU and there is no Granger-causality in the reverse direction