Measuring Economic Policy Uncertainty Using an Unsupervised Word Embedding-based Method

TL;DR

This paper introduces an unsupervised word embedding-based method to measure Economic Policy Uncertainty from news articles, overcoming limitations of keyword-based approaches and effectively capturing economic events.

Contribution

It presents a novel, unsupervised approach using word embeddings to identify relevant keywords for EPU, eliminating the need for pre-defined dictionaries and improving cross-country applicability.

Findings

The method accurately tracks major economic events in Iran.

EPU series correlates well with the World Uncertainty Index.

The approach is robust to semantic similarity thresholds.

Abstract

Economic Policy Uncertainty (EPU) is a critical indicator in economic studies, while it can be used to forecast a recession. Under higher levels of uncertainty, firms' owners cut their investment, which leads to a longer post-recession recovery. EPU index is computed by counting news articles containing pre-defined keywords related to policy-making and economy and convey uncertainty. Unfortunately, this method is sensitive to the original keyword set, its richness, and the news coverage. Thus, reproducing its results for different countries is challenging. In this paper, we propose an unsupervised text mining method that uses word-embedding representation space to select relevant keywords. This method is not strictly sensitive to the semantic similarity threshold applied to the word embedding vectors and does not require a pre-defined dictionary. Our experiments using a massive repository of Persian news show that the EPU series computed by the proposed method precisely follows major events affecting Iran's economy and is compatible with the World Uncertainty Index (WUI) of Iran.