Dynamic Causal Effects of Pandemic-induced Uncertainty on Output, Credit, and Asset Prices: A Symbolic Transfer Entropy Approach

Abstract

The purpose of this paper is to examine at information transfer between pandemic-induced indices of economic policy uncertainty and economic factors, such as credit growth, economic activity, and asset prices. For this purpose, we implement a Symbolic Transfer Entropy approach proposed by Camacho et al. (2021), which represents an improved specification for detecting Granger-type causality in panel datasets. Specifically, this procedure yields a more robust specification when linearity assumptions break down, when in the presence of structural breaks, or when the data generating process is heterogeneous across the cross-section units. Aggregate economic variables are compared against the world pandemic uncertainty index (WPUI), which is based on the work of Ahir, Bloom, and Furceri (2020) and measures economic uncertainty related to pandemics and other disease outbreaks across the world. Amidst concerns that past economic shocks have left permanent scars on long-term growth, known as hysteresis, we find no robust evidence of a long-run causal effect between pandemic-induced uncertainty and most of key aggregate economic variables. The results have implications for the relevance of economic uncertainty to long-term economic outcomes.