Diakonova, M., Molina, L., Mueller, H., Pérez, J. J., Rauh, C.
The Information Content of Conflict, Social Unrest and Policy Uncertainty Measures for Macroeconomic Forecasting
CWPE2418
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 the traditional lack of availability of timely 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 using different methods. Our results strongly suggest that capturing institutional instability above a broad set of standard high-frequency indicators is useful when forecasting quarterly GDP. We also analyse relative strengths and weaknesses of the approach.
Keywords: Forecasting, Forecasting GDP, Geopolitical Risk, Natural Language Processing, Policy Uncertainty, Social Conflict, Social Unrest
JEL Codes: E37 D74 N16
Author links: Christopher Rauh
PDF: https://www.econ.cam.ac.uk/research-files/repec/cam/pdf/cwpe2418.pdf 
