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

Giovanni Cicceri, Giuseppe Inserra and Michele Limosani
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Giovanni Cicceri: Department of Engineering, University of Messina, 98166 Messina, Italy
Giuseppe Inserra: Department of Economics, University of Messina, 98122 Messina, Italy
Michele Limosani: Department of Economics, University of Messina, 98122 Messina, Italy

Mathematics, 2020, vol. 8, issue 2, 1-20

Abstract: 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 (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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