Carrasco Sánchez, Carlos Alberto2020-06-042020-06-042019-11-20Delgado de la Garza Luis Alberto, Garza Rodríguez Gonzalo Adolfo, Jacques Osuna Daniel Alejandro, Múgica Lara Alejandro. Does the use of big data variables improve the prediction of Monetary Policy decisions? The case of Mexico. San Pedro Garza García, 2019, 42 p. Tesis (Licenciatura en Economía). Universidad de Monterrey, Escuela de Negocios.http://repositorio.udem.edu.mx/handle/61000/7We analyzed the predictive power of a market attention variable, generated using big data, for Banco de Mexico’s (Mexican central bank, hereby “Banxico”) monetary policy decisions. The novelty of this paper relies on the lack of previous research that incorporates a nonconventional variable that uses big data analysis in monetary policy research. We used a binary probit approach and contrasted different models to identify whether the proposed variable improved the prediction. Our general results show there is significant evidence that the variable improves the prediction, as it helps reduce information criteria and it stays significant across the different models. We consider that further research is necessary to determine the scope of big data in monetary policy analysis prediction.42 páginasespMonetary policy decisionsBig DataBanxicoInterest ratesEconomíaMacroeconomíabachelorThesisLuis Alberto Delgado de la Garza 000192414, Gonzalo Adolfo Garza Rodríguez 000352119, Daniel Alejandro Jacques Osuna 000523025, Alejandro Múgica Lara 000345409