Monetary policy uncertainty in Mexico: an unsupervised approach

We study and measure the uncertainty in the Spanish version of the minutes of the meetings of the Governing Board of the Bank of Mexico and relate it to key monetary policy variables. These minutes summarize the information about the domestic and international economic, inflation and financial background, as well as the reasoning and rationale behind the chosen monetary policy decision. In particular, we conceive various uncertainty indices using unsupervised machine learning techniques for natural language processing and a large language model. A first set of uncertainty indices is constructed by exploiting latent Dirichlet allocation (LDA), whereas a second set is based on word embedding (implemented with the skip-gram (SG) model) and k-means. Finally, a third set of uncertainty indices is based on ChatGPT (GPT). For each of these three approaches, we create an uncertainty index for the whole set of minutes, and three section-specific uncertainty indices for the three main sections of the minutes. Then, we obtain an overall Monetary Policy Uncertainty (MPU) index and three section-specific indices by averaging the corresponding LDA, SG, and GPT indices. Thus, with the implementation of an SVAR model, we find that a positive shock in the MPU index is related to an increase in money supply, in the consumer price index, in the target for the overnight interbank interest rate, and to a depreciation of the Mexican peso.