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A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study
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| Zeitschriftentitel: | Mathematics |
|---|---|
| Personen und Körperschaften: | , , |
| In: | Mathematics, 8, 2020, 2, S. 241 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
MDPI AG
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| Schlagwörter: |
| author_facet |
Cicceri, Giovanni Inserra, Giuseppe Limosani, Michele Cicceri, Giovanni Inserra, Giuseppe Limosani, Michele |
|---|---|
| author |
Cicceri, Giovanni Inserra, Giuseppe Limosani, Michele |
| spellingShingle |
Cicceri, Giovanni Inserra, Giuseppe Limosani, Michele Mathematics A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study General Mathematics Engineering (miscellaneous) Computer Science (miscellaneous) |
| author_sort |
cicceri, giovanni |
| spelling |
Cicceri, Giovanni Inserra, Giuseppe Limosani, Michele 2227-7390 MDPI AG General Mathematics Engineering (miscellaneous) Computer Science (miscellaneous) http://dx.doi.org/10.3390/math8020241 <jats:p>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.</jats:p> A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study Mathematics |
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| title |
A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study |
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A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study |
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A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study |
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A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study |
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A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study |
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A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study |
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a machine learning approach to forecast economic recessions—an italian case study |
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General Mathematics Engineering (miscellaneous) Computer Science (miscellaneous) |
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http://dx.doi.org/10.3390/math8020241 |
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2020 |
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<jats:p>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.</jats:p> |
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| author | Cicceri, Giovanni, Inserra, Giuseppe, Limosani, Michele |
| author_facet | Cicceri, Giovanni, Inserra, Giuseppe, Limosani, Michele, Cicceri, Giovanni, Inserra, Giuseppe, Limosani, Michele |
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| description | <jats:p>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.</jats:p> |
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| spelling | Cicceri, Giovanni Inserra, Giuseppe Limosani, Michele 2227-7390 MDPI AG General Mathematics Engineering (miscellaneous) Computer Science (miscellaneous) http://dx.doi.org/10.3390/math8020241 <jats:p>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.</jats:p> A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study Mathematics |
| spellingShingle | Cicceri, Giovanni, Inserra, Giuseppe, Limosani, Michele, Mathematics, A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study, General Mathematics, Engineering (miscellaneous), Computer Science (miscellaneous) |
| 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 | a machine learning approach to forecast economic recessions—an italian case study |
| title_unstemmed | A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study |
| topic | General Mathematics, Engineering (miscellaneous), Computer Science (miscellaneous) |
| url | http://dx.doi.org/10.3390/math8020241 |