Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/336410 
Year of Publication: 
2024
Citation: 
[Journal:] Latin American Journal of Central Banking (LAJCB) [ISSN:] 2666-1438 [Volume:] 5 [Issue:] 4 [Article No.:] 100130 [Year:] 2024 [Pages:] 1-15
Publisher: 
Elsevier, Amsterdam
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.
Subjects: 
Forecasting
Forecasting GDP
Geopolitical risk
Natural language processing
Policy uncertainty
Social conflict
Social unrest
JEL: 
E37
D74
N16
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article

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