MACHINE LEARNING MODELS IN ECONOMETRIC ANALYSIS ACROSS CENTRAL
DOI:
https://doi.org/10.5281/zenodo.23117933Abstract
This study compares machine learning and panel econometric models for forecasting GDP growth across Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan. Comparable annual indicators were constructed on a consistent basis, stationarity was assessed using the ADF test, panel models were estimated, and predictive performance was evaluated through cross-validation. Driscoll–Kraay inference and country-specific robustness checks were employed to assess the stability of the results. Regularized and boosting models demonstrated stronger out-of-sample predictive performance, while panel estimation retained clearer coefficient-level interpretability. The study proposes an integrated validation framework that combines forecasting performance with economic interpretation in the context of heterogeneous Central Asian data.Keywords
ADF stationarity test, Central Asia, Driscoll–Kraay inference, forecast stability, panel data, predictive modeling, regularized algorithmsReferences
Varian H., R. (2014). Big data: New tricks for econometrics. Journal of Economic Perspectives, 28(2), 3–28. https://doi.org/10.1257/jep.28.2.3
Mullainathan, S., & Spiess, J. (2017). Machine learning: An applied econometric approach. Journal of Economic Perspectives, 31(2), 87–106. https://doi.org/10.1257/jep.31.2.87
Athey, S., & Imbens G., W. (2019). Machine learning methods that economists should know about. Annual Review of Economics, 11, 685–725. https://doi.org/10.1146/annurev-economics-080217-053433
Chernozhukov, V., Chetverikov, D., Demirer, M., et al. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1). https://doi.org/10.1111/ectj.12097
Belloni, A., Chernozhukov, V., & Hansen, C. (2014). Inference on treatment effects after selection among high-dimensional controls. The Review of Economic Studies, 81(2), 608–650. https://doi.org/10.1093/restud/rdt044
Varian H., R. (2016). Causal inference in economics and marketing. Proceedings of the National Academy of Sciences, 113(27), 7310–7315. https://doi.org/10.1073/pnas.1510479113
Musayeva D., A. (2022). Iqtisodiy jarayonlarni ekonometrik modellashtirishda mashinali o‘qitish usullaridan foydalanish. Iqtisodiyot va innovatsion texnologiyalar, (6).
Fayzullayev Sh., R. (2023). Markaziy Osiyo mamlakatlari iqtisodiy ko‘rsatkichlarini prognozlashda sun’iy intellekt algoritmlarining qo‘llanilishi. Iqtisodiy taraqqiyot va tahlil, (4).
Athey, S., & Imbens G., W. (2017). The state of applied econometrics: Causality and policy evaluation. Journal of Economic Perspectives, 31(2), 3–32. https://doi.org/10.1257/jep.31.2.3
Chernozhukov, V., Demirer, M., Dufour, A., & Fernández-Val, I. (2018). Generic machine learning inference on heterogeneous treatment effects in randomized experiments. arXiv. https://doi.org/10.48550/arXiv.1712.04802
Mullainathan, S., & Obermeyer, Z. (2022). Does machine learning automate moral hazard and error correction?. American Economic Review, 112(5), 1513–1540. https://doi.org/10.1257/aer.20191700
Athey, S. (2019). The impact of machine learning on economics. The Economics of Artificial Intelligence: An Agenda, 507–547. https://doi.org/10.7208/chicago/9780226613475.003.0019
Клейнер Г., Б. (2021). Системная сбалансированность экономики: методология и модели. Экономика и математические методы, 57(1), 3–15.
Айвазян С., А., & Мхитарян В., С. (2019). Прикладная статистика и основы эконометрики. ЮНИТИ-ДАНА.
Балацкий Е., В., & Екимова Н., А. (2022). Искусственный интеллект и экономический рост: новые возможности эмпирического анализа. Вестник Российской академии наук, 92(9), 835–844.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 MUHANDISLIK VA IQTISODIYOT
This work is licensed under a Creative Commons Attribution 4.0 International License.
