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Open AccessArticle

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

1
Department of Engineering, University of Messina, Messina 98166, Italy
2
Department of Economics, University of Messina, Messina 98122, Italy
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Author to whom correspondence should be addressed.
Mathematics 2020, 8(2), 241; https://doi.org/10.3390/math8020241
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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