A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study
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 th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-02-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/8/2/241 |
| _version_ | 1818035097506414592 |
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| author | Giovanni Cicceri Giuseppe Inserra Michele Limosani |
| author_facet | Giovanni Cicceri Giuseppe Inserra Michele Limosani |
| author_sort | Giovanni Cicceri |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T06:49:38Z |
| format | Article |
| id | doaj.art-0a953a7153d54f4a965e9f50092dc8d6 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-12-10T06:49:38Z |
| publishDate | 2020-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-0a953a7153d54f4a965e9f50092dc8d62022-12-22T01:58:34ZengMDPI AGMathematics2227-73902020-02-018224110.3390/math8020241math8020241A Machine Learning Approach to Forecast Economic Recessions—An Italian Case StudyGiovanni Cicceri0Giuseppe Inserra1Michele Limosani2Department of Engineering, University of Messina, 98166 Messina, ItalyDepartment of Economics, University of Messina, 98122 Messina, ItalyDepartment of Economics, University of Messina, 98122 Messina, ItalyIn 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.https://www.mdpi.com/2227-7390/8/2/241economic recessionsgdpmachine learninglevenberg-marquardtforecasting |
| spellingShingle | Giovanni Cicceri Giuseppe Inserra Michele Limosani A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study Mathematics economic recessions gdp machine learning levenberg-marquardt forecasting |
| title | A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study |
| title_full | A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study |
| title_fullStr | A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study |
| title_full_unstemmed | A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study |
| title_short | A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study |
| title_sort | machine learning approach to forecast economic recessions an italian case study |
| topic | economic recessions gdp machine learning levenberg-marquardt forecasting |
| url | https://www.mdpi.com/2227-7390/8/2/241 |
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