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Proyección de la Inflación en Chile con Métodos de Machine Learning

Felipe Leal, Carlos Molina and Eduardo Zilberman

Working Papers Central Bank of Chile from Central Bank of Chile

Abstract: In this paper, in line with Medeiros et al. (2019) for the US, we apply Machine Learning (ML) methods with Big Data to forecast the total and underlying CPI inflation in Chile. We show that the ML methods do not gain in the inflation projection for the Chilean case in a consistent way on simple and univariate linear competitors such as the AR, the mean and the median of the past inflation, which have proven to be highly competitive. In fact, these are the winning methods in many cases. A second contribution of this work is the construction of a large dataset with macroeconomic variables related to the Chilean economy similar to McCracken and Ng (2016), who built (and maintains) a similar data for the United States.

Date: 2020-01
New Economics Papers: this item is included in nep-big, nep-lam and nep-mac
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