Forecast the inflation rate in Lebanon: The use of the artificial neural networks method

Jean-François Verne ()
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Jean-François Verne: Université Saint-Joseph de Beyrouth, Faculté de Sciences Economiques

Economics Bulletin, 2022, vol. 42, issue 4, 1798 - 1810

Abstract: In this article, we use the neural network method to analyze the inflation rate evolution in Lebanon between December 2008 to January 2022. We particularly use the Nonlinear Autoregressive model, called NAR, which belongs to the models of Artificial Neural Networks (ANN), to make predictions of the inflation rate in Lebanon over a twelve-month period. Then, we compare the goodness of fit of these predictions from such a model with that obtained by the seasonal ARIMA model. Thus, the NAR model generates better results, in terms of forecasting and adjustment of the estimated data to the observed data than those achieved with the linear seasonal ARIMA model.

Keywords: Inflation rate; Neural Networks; Forecast; Autoregressive models (search for similar items in EconPapers)
JEL-codes: C2 E3 (search for similar items in EconPapers)
Date: 2022-12-30
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