Abstract

CRUZ MARTINEZ, Eduardo de Jesús; AVILA ROMERO, Rita  and  CHIATCHOUA, Cesaire. Analysis of inflation and economic growth: a Machine Learning approach. México 1990-2021. Universidad y Sociedad [online]. 2024, vol.16, n.3, pp. 174-188.  Epub June 30, 2024. ISSN 2218-3620.

The objective of this study is to analyze the determinants of inflation and economic growth in order to have variables that help improve forecasts. The Method uses the recurrent neural network model for the prediction of variables added to a comparison of the results of an ARIMA model. Among the main results, it was found that neural network models tend to have better performance in forecasting, unlike ARIMA models. And as conclusions, it is determined that neural network models can improve their results by manipulating the hyper parameters of the model with other data, as well as considering more variables that have been key to inflation and economic growth.

Keywords : Inflation; Economic growth; Models; Machine Learning; Forecasting; Neural network.

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