The information content of conflict, social unrest and policy uncertainty measures for macroeconomic forecasting
Series: Working Papers. 2232.
Author: Marina Diakonova, Luis Molina, Hannes Mueller, Javier J. Pérez and Cristopher Rauh.
Published in Latin American Journal of Central Banking, v. 5, Issue 4, December 2024, 100130
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Abstract
It is widely accepted that episodes of social unrest, conflict, political tensions and policy uncertainty affect the economy. Nevertheless, the real-time dimension of such relationships is less studied, and it remains unclear how to incorporate them in a forecasting framework. This can be partly explained by a certain divide between the economic and political science contributions in this area, as well as by the traditional lack of availability of high-frequency indicators measuring such phenomena. The latter constraint, though, is becoming less of a limiting factor through the production of text-based indicators. In this paper we assemble a dataset of such monthly measures of what we call “institutional instability”, for three representative emerging market economies: Brazil, Colombia and Mexico. We then forecast quarterly GDP by adding these new variables to a standard macro-forecasting model in a mixed-frequency MIDAS framework. Our results strongly suggest that capturing institutional instability based on a broad set of standard high-frequency indicators is useful when forecasting quarterly GDP. We also analyse the relative strengths and weaknesses of the approach.