Economic Growth and Fiscal Policy in Peru: Prediction Using Machine Learning Models
The empirical literature presents several indicators related to fiscal policy and economic growth. The paper aims to predict Peru's economic growth using fiscal policy variables. For this purpose, open data from the Central Reserve Bank of Peru was used, data preprocessing and the study used Py...
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| Formato: | Artículo |
| Lenguaje: | inglés |
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Science and Information Organization
2025
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| _version_ | 1881508883471532032 |
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| author | Ramos, Fidel Huanco Ccori, Yesenia Valentin Lloclla, Henry Shuta Sotomayor, Martha Yucra Mamani Uchasara, Ilda |
| author_browse | Ccori, Yesenia Valentin Lloclla, Henry Shuta Mamani Uchasara, Ilda Ramos, Fidel Huanco Sotomayor, Martha Yucra |
| author_facet | Ramos, Fidel Huanco Ccori, Yesenia Valentin Lloclla, Henry Shuta Sotomayor, Martha Yucra Mamani Uchasara, Ilda |
| author_sort | Ramos, Fidel Huanco |
| collection | Producción científica |
| description | The empirical literature presents several indicators related to fiscal policy and economic growth. The paper aims to predict Peru's economic growth using fiscal policy variables. For this purpose, open data from the Central Reserve Bank of Peru was used, data preprocessing and the study used Python programming through Google Colab to evaluate eight machine learning models. Metrics such as Root Mean Square Error (RMSE), Mean absolute error (MAE), Mean square error (MSE), and Coefficient of Determination (R²) were used to measure their performance. In addition, SHapley Additive exPlanations (SHAP) was applied to interpret the importance of macroeconomic variables. The results show that the K-Nearest Neighbors (KNN) model obtained the best performance, with an R² of 0.972 and low prediction errors. In the same way, important variables in fiscal policy such as Net Debt, Liabilities, and Interest on External Debt were identified. In conclusion, the study shows that KNN and Ensemble Bagging are highly effective models for predicting Peru's economic growth. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/2903 |
| institution | UPeU |
| language | English |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Science and Information Organization |
| publisherStr | Science and Information Organization |
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| spelling | oai:cris.upeu.edu.pe:20.500.15037/29032026-10-30T16:19:08ZEconomic Growth and Fiscal Policy in Peru: Prediction Using Machine Learning ModelsRamos, Fidel HuancoCcori, Yesenia ValentinLloclla, Henry ShutaSotomayor, Martha YucraMamani Uchasara, IldaMachine learningpredictive modelsfiscal policyeconomic growthThe empirical literature presents several indicators related to fiscal policy and economic growth. The paper aims to predict Peru's economic growth using fiscal policy variables. For this purpose, open data from the Central Reserve Bank of Peru was used, data preprocessing and the study used Python programming through Google Colab to evaluate eight machine learning models. Metrics such as Root Mean Square Error (RMSE), Mean absolute error (MAE), Mean square error (MSE), and Coefficient of Determination (R²) were used to measure their performance. In addition, SHapley Additive exPlanations (SHAP) was applied to interpret the importance of macroeconomic variables. The results show that the K-Nearest Neighbors (KNN) model obtained the best performance, with an R² of 0.972 and low prediction errors. In the same way, important variables in fiscal policy such as Net Debt, Liabilities, and Interest on External Debt were identified. In conclusion, the study shows that KNN and Ensemble Bagging are highly effective models for predicting Peru's economic growth.Article10.14569/IJACSA.2025.01604052-s2.0-105004364723https://hdl.handle.net/20.500.15037/2903enInternational Journal of Advanced Computer Science and ApplicationsInternational Journal of Advanced Computer Science and Applications2158-107XScience and Information Organization2025-01-012026-06-27T02:34:38Z2026-06-27T02:34:38Z |
| spellingShingle | Machine learning predictive models fiscal policy economic growth Ramos, Fidel Huanco Ccori, Yesenia Valentin Lloclla, Henry Shuta Sotomayor, Martha Yucra Mamani Uchasara, Ilda Economic Growth and Fiscal Policy in Peru: Prediction Using Machine Learning Models |
| title | Economic Growth and Fiscal Policy in Peru: Prediction Using Machine Learning Models |
| title_full | Economic Growth and Fiscal Policy in Peru: Prediction Using Machine Learning Models |
| title_fullStr | Economic Growth and Fiscal Policy in Peru: Prediction Using Machine Learning Models |
| title_full_unstemmed | Economic Growth and Fiscal Policy in Peru: Prediction Using Machine Learning Models |
| title_short | Economic Growth and Fiscal Policy in Peru: Prediction Using Machine Learning Models |
| title_sort | economic growth and fiscal policy in peru prediction using machine learning models |
| topic | Machine learning predictive models fiscal policy economic growth |
| topic_facet | Machine learning predictive models fiscal policy economic growth |
| url | https://hdl.handle.net/20.500.15037/2903 |
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