Modeling Inflation and Rupiah Exchange Rate Responses Using Bootstrap Aggregating Multivariate Adaptive Regression Spline in Indonesia

Revika Inta Nur Kholifah; Tiani Wahyu Utami; Ali Imron

doi:10.31764/jtam.v10i3.37314

Modeling Inflation and Rupiah Exchange Rate Responses Using Bootstrap Aggregating Multivariate Adaptive Regression Spline in Indonesia

Revika Inta Nur Kholifah, Tiani Wahyu Utami, Ali Imron

Abstract

This study aims to evaluate the performance of the Bootstrap Aggregating (Bagging) method applied to Multivariate Adaptive Regression Splines (MARS) in improving the predictive ability of biresponse models, compared to biresponse MARS models without bagging, using a case study of inflation and the rupiah exchange rate in Indonesia. Inflation and exchange rates are important macroeconomic indicators that are interrelated and play a crucial role in maintaining economic stability; therefore, a prediction model capable of accurately capturing simultaneous relationships and nonlinear patterns is required. The contribution of this study lies in the application of a biresponse nonparametric regression framework based on bagging to simultaneously model biresponse variables, which has rarely been explored in previous research that generally focuses on a single-response approach. The biresponse approach is used to accommodate the interrelationship between response variables, while the bagging procedure is implemented through a bootstrap technique with several replication scenarios to reduce prediction variance and improve model stability. The final prediction is obtained by averaging the results from all bootstrap models formed. The results of this study indicate that the application of Bagging MARS with 100 replications can significantly improve model performance, as shown by a decrease in the RMSE value from 132.40 to 92.08 and MAE from 70.71 to 52.12, as well as an increase in the R² value from 0.9997096 to 0.9998597. These findings indicate that the integration of the bootstrap technique in the Bagging MARS approach is effective in reducing model variability and producing more stable predictions. Practically, the Bagging MARS method has the potential to be used as an alternative in modeling interrelated macroeconomic indicators with nonlinear characteristics.

Keywords

Multivariate Adaptive Regression Splines; Bootstrap Aggregating MARS; Biresponse; Inflation; Rupiah Exchange Rate.

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References

Aizenman, J., Chinn, M. D., & Ito, H. (2016). Monetary policy spillovers and the trilemma in the new normal: Periphery country sensitivity to core country conditions. Journal of International Money and Finance, 68, 298–330. https://doi.org/https://doi.org/10.1016/j.jimonfin.2016.02.008

Arisanti, R., & Puspita, M. D. (2022). Non-Linear Autoregressive Neural Network With Exogenous Variable In Forecasting Usd/Idr Exchange Rate. Communications in Mathematical Biology and Neuroscience, 2022. Article ID 5. https://doi.org/10.28919/cmbn/6931

Benn Steil. (2025). Global Inflation Tracker. Council on Foreign Relations, Greenberg Center for Geoeconomic Studies. https://www.cfr.org/tracker/global-inflation-tracker

Çatal, M. İ., Celik, S., & Bakoglu, A. (2023). Practicability of MARS and Bagging MARS Algorithms in Prediction of Plant Length of Grass Pea (Lathyrus sativus L.) in Turkey. Acta Physiologiae Plantarum, 45(9), 112. https://doi.org/10.1007/s11738-023-03587-8

de Oliveira Celeri, M., da Costa, W. G., Nascimento, A. C. C., Azevedo, C. F., Cruz, C. D., Sagae, V. S., & Nascimento, M. (2024). Multivariate Adaptive Regression Splines Enhance Genomic Prediction of Non-Additive Traits. Agronomy, 14(10), 2234. https://doi.org/10.3390/agronomy14102234

Efendi, A., Amrullah, A. A. N., Fitriani, R., & Rahayudi, B. (2021). Analysis And Simulation Of Accuracy Of Credit Status Classification With Bootstrap Aggregating (Bagging) And Synthetic Minority Over-Sampling (Smote). Int. J. Agricult. Stat. Sci, 17(01-Supp), 925–938, 2021. https://connectjournals.com/03899.2021.17.925

Eskandarinejad, A., Arellano, D., Nazari, R., & Nikoo, M. R. (2025). Multivariate Adaptive Regression Splines (MARS) Analysis And Complementary Machine Learning Approaches In Predicting Earthquake-Induced Slope Displacements. Earth Science Informatics, 18(2), 398. https://doi.org/10.1007/s12145-025-01899-6

Friedman, J. H. (1991). Multivariate Adaptive Regression Spline. The Annals of Statistics, 19(1), 1–67. https://doi.org/10.1214/aos/1176347963

Hashmi, S. M., Gilal, M. A., & Wong, W.-K. (2021). Sustainability of Global Economic Policy and Stock Market Returns in Indonesia. Sustainability, 13(10), 5422. https://doi.org/10.3390/su13105422

Indrawati, S. M., Satriawan, E., & Abdurohman. (2024). Indonesia’s Fiscal Policy in the Aftermath of the Pandemic. Bulletin of Indonesian Economic Studies, 60(1), 1–33. https://doi.org/10.1080/00074918.2024.2335967

Knicker, M. S., Naumann-Woleske, K., Bouchaud, J.-P., & Zamponi, F. (2025). Post-COVID Inflation & the Monetary Policy Dilemma: An Agent-Based Scenario Analysis. Journal of Economic Interaction and Coordination, 20, 141-195. https://doi.org/10.1007/s11403-024-00413-3

Kulekçi, M., Eyduran, E., Altın, A. Y., & Tariq, M. M. (2022). Usefulness of MARS and Bagging MARS Algorithms in Prediction of Honey Production in Beekeeping Enterprises from Elazig Province of Turkey. Pakistan Journal of Zoology, 54(3), 1087–1093. https://doi.org/10.17582/journal.pjz/20200309160354

Liu, Y., Li, D., & Xia, Y. (2023). Dimension Reduction and MARS. In Journal of Machine Learning Research (Vol. 24), Article 309. https://doi.org/https://doi.org/10.48550/arXiv.2302.05790

Mirza, N., Naqvi, B., Rizvi, S. K. A., & Boubaker, S. (2023). Exchange rate pass-through and inflation targeting regime under energy price shocks. Energy Economics, 124, 106761. https://doi.org/10.1016/j.eneco.2023.106761

Otok, B. W., Putra, R. Y., & P Yasmirullah, S. D. (2020). Bootstrap Aggregating Multivariate Adaptive Regression Spline For Observational Studies In Diabetes Cases. In Systematic Reviews in Pharmacy (Vol. 11, Number 8), 406-413. https://doi.org/10.31838/srp.2020.8.59

Panggabean, S. M. U., Ekananda, M., Gitaharie, B. Y., & Djuranovik, L. (2025). Steadying the Ship: Can Export Proceeds Repatriation Policy Stabilize Indonesian Exchange Rates Amid Short-Term Capital Flow Fluctuations? Economies, 13(6), Article 180. https://doi.org/10.3390/economies13060180

Priambodo, D., Wasono, R., & Al Haris, M. (2024). Modeling of Poverty Indicators in East Java Province Using Bootstrap Aggregating Multivariate Adaptive Regression Spline (Bagging MARS). Jurnal Statistika Universitas Muhammadiyah Semarang, 12(2), 19–28, https://doi.org/10.14710/JSUNIMUS.12.2.2024.19-28

Rustam, H., Suhadak, Sri Mangesti, R., & Siti Ragil, H. (2019). The Effect Of Macroeconomic And Financial Performance On Systematic Risk And Stock Returns: Study On Companies Of Basic Industry And Chemicals Listed On The Indonesia Stock Exchange During Period Of 2011-2015. Eurasia: Economics & Business, 7(25), 28-42. https://doi.org/10.18551/econeurasia.2019-07

Sabancı, D., & Cengiz, M. A. (2022). Random Ensemble MARS: Model Selection in Multivariate Adaptive Regression Splines Using Random Forest Approach. Journal of New Theory, (40), 27–45. https://doi.org/10.53570/jnt.1147323

ŞEVGİN, H. (2023). A Comparative Study of Ensemble Methods in the Field of Education: Bagging and Boosting Algorithms. International Journal of Assessment Tools in Education, 10(3), 544–562. https://doi.org/10.21449/ijate.1167705

Sriningsih, R., Otok, B. W., & Sutikno. (2023). Determination of the Best Multivariate Adaptive Geographically Weighted Generalized Poisson Regression Splines Model Employing Generalized Cross-Validation in Dengue Fever Cases. In MethodsX (Vol. 10), Article 102174. Elsevier B.V. https://doi.org/10.1016/j.mex.2023.102174

Sumantri, V. D. S., & Fadli, F. (2022). Analysis of Macroeconomic Variables Affecting Inflation and Exchange Rates. Integrated Journal of Business and Economics, 6(2), 102. https://doi.org/10.33019/ijbe.v6i2.417

Uysal, F., & Sonmez, R. (2023). Bootstrap Aggregated Case-Based Reasoning Method for Conceptual Cost Estimation. Buildings, 13(3), Article 651. https://doi.org/10.3390/buildings13030651

Venkatesan, T., & Ponnamma, M. S. (2017). An Analysis of Macroeconomic Factors Affecting Foreign Exchange Rate. SDMIMD Journal of Management, 8(1), 21. https://doi.org/10.18311/sdmimd/2017/15716

Yu, Z., Farooq, U., Shukurullaevich, N. K., Alam, M. M., & Dai, J. (2024). How Does Inflation Rate Influence The Resource Utilization Policy? New Empirical Evidence From OPEC Countries. Resources Policy, 91, 104862. https://doi.org/https://doi.org/10.1016/j.resourpol.2024.104862

DOI: https://doi.org/10.31764/jtam.v10i3.37314