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 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.

@article{Ramos2025,
  title     = {Economic Growth and Fiscal Policy in Peru: Prediction Using Machine Learning Models},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {4},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Fidel Huanco Ramos and Yesenia Valentin Ccori and Henry Shuta Lloclla and Martha Yucra Sotomayor and Ilda Mamani Uchasara},
  doi       = {10.14569/IJACSA.2025.0160405},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160405}
}