The information content of conflict, social unrest and policy uncertainty measures for macroeconomic forecasting

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
• Social unrest
• Social conflict
• Policy uncertainty
• Forecasting GDP
• Natural language processing