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Open AccessArticle

A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study

Department of Engineering, University of Messina, Messina 98166, Italy
Department of Economics, University of Messina, Messina 98122, Italy
Author to whom correspondence should be addressed.
Mathematics 2020, 8(2), 241;
Received: 20 December 2019 / Revised: 6 February 2020 / Accepted: 6 February 2020 / Published: 13 February 2020
In economic activity, recessions represent a period of failure in Gross Domestic Product (GDP) and usually are presented as episodic and non-linear. For this reason, they are difficult to predict and appear as one of the main problems in macroeconomics forecasts. A classic example turns out to be the great recession that occurred between 2008 and 2009 that was not predicted. In this paper, the goal is to give a different, although complementary, approach concerning the classical econometric techniques, and to show how Machine Learning (ML) techniques may improve short-term forecasting accuracy. As a case study, we use Italian data on GDP and a few related variables. In particular, we evaluate the goodness of fit of the forecasting proposed model in a case study of the Italian GDP. The algorithm is trained on Italian macroeconomic variables over the period 1995:Q1-2019:Q2. We also compare the results using the same dataset through Classic Linear Regression Model. As a result, both statistical and ML approaches are able to predict economic downturns but higher accuracy is obtained using Nonlinear Autoregressive with exogenous variables (NARX) model.
Keywords: economic recessions; GDP; machine learning; levenberg-marquardt; forecasting economic recessions; GDP; machine learning; levenberg-marquardt; forecasting
MDPI and ACS Style

Cicceri, G.; Inserra, G.; Limosani, M. A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study. Mathematics 2020, 8, 241.

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