O‘zbekistonda iqtisodiy jarayonlarni tadqiq etishda “machine learning” modellaridan foydalanish masalalari
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Published:
Jun 26, 2020
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Это произведение доступно по лицензии Creative Commons «Attribution-ShareAlike» («Атрибуция — На тех же условиях») 4.0 Всемирная
Это произведение доступно по лицензии Creative Commons «Attribution-ShareAlike» («Атрибуция — На тех же условиях») 4.0 Всемирная
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Статьи
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Abstract
This paper proposes a new type of solution for Uzbekistan economy using several machine learning methods: LASSO, Ridge, Random Forest, Gradient Boosting and Artificial Neural Networks. This paper is one of the first attempts to apply machine learning methods to the macroeconomic forecasting in Uzbekistan. The main result of this paper is the confirmation of the possibility of more accurate forecasting of economic indicators in Uzbekistan using machine learning methods.
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How to Cite
Hakimov, H. (2020). O‘zbekistonda iqtisodiy jarayonlarni tadqiq etishda “machine learning” modellaridan foydalanish masalalari. Scientific Research Archive, 1(11). Retrieved from https://ejournal.tsue.uz/index.php/archive/article/view/1544
References
1. Phillips Curve Inflation Forecasts James H. Stock, Mark W. Watson NBER Working Paper No. 14322 September 2008
2. Inflation forecasting; Faust, Jon; Jonathan H. Wright; Handbook of Forecasting, 2:A, 2013, G. Elliott and A. Timmermann eds., 2013, 2-56.)
3. Fokin, N. and Polbin, A. (2019). Forecasting Russia's Key Macroeconomic Indicators with the VAR-LASSO Model. Russian Journal of Money and Finance, 78(2), pp. 67–93.
4. Yun Liao, 2017. "Machine Learning in Macro-Economic Series Forecasting," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 9(12), pages 71-76, December.
5. Siami-Namini, Sima et al. “A Comparison of ARIMA and LSTM in Forecasting Time Series.” 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) (2018): 1394-1401.
6. Nonlinear Inflation Forecasting with Recurrent Neural Networks Anna Almosova Niek Andresen (2019)
7. Hakimov, Hakimjon. 2020. Issues of optimic basis of public financing in following financial and monetary policy. https://journal.tsue.uz/index.php/archive/article/view/927
8. Ҳакимов, Ҳакимжон. 2019. «Макроиқтисодий барқарорликни таъминлашда фискал ва монетар сиёсатни мувофиқлаштириш зарурати». https://journal.tsue.uz/index.php/archive/article/view/627
9. Махмудов, Носир; Ҳакимов, Ҳакимжон. 2017. «Макроиқтисодий барқарорликни мустаҳкамлаш- барқарор иқтисодий ўсишни таъминлашнинг муҳим шарти». https://journal.tsue.uz/index.php/archive/article/view/674.
10. Баҳриддинов, В.; Ҳакимов, Ҳ. (2018). Инфляцияни таргетлаш сиёсатини амалга оширишнинг хориж тажрибаси. https://journal.tsue.uz/index.php/archive/article/view/666
11. Ҳакимов, Ҳакимжон. 2018. «Мамлакатда фискал ва монетар сиёсатни мувофиқлаштиришнинг моҳияти ва сабаблари». https://journal.tsue.uz/index.php/archive/article/view/224.
12. Махмудов Н.М, Ҳакимов Ҳ.А. "Иқтисодий ўсиш моделлари ва улардан макроиқтисодий таҳлилда фойдаланиш йўналишлари." Экономика и финансы (Узбекистан) 3 (2016).
13. Хакимов, Х. А. (2016). Роль государства в обеспечении макроэкономической стабилности. Ученый XXI века, (5-2)
14. Ҳакимов, Ҳ. (2020). Инқироз ва ундан кейинги шароитлар учун давлат қарзларининг юқори чегараси. https://journal.tsue.uz/index.php/archive/article/view/1038
15. Баҳриддинов, В. (2019). Инфляцион таргетлаш сиёсатинининг хусусиятлари ҳамда уни ўзбекистонда жорий этиш истиқболлари. https://journal.tsue.uz/index.php/archive/article/view/1079
2. Inflation forecasting; Faust, Jon; Jonathan H. Wright; Handbook of Forecasting, 2:A, 2013, G. Elliott and A. Timmermann eds., 2013, 2-56.)
3. Fokin, N. and Polbin, A. (2019). Forecasting Russia's Key Macroeconomic Indicators with the VAR-LASSO Model. Russian Journal of Money and Finance, 78(2), pp. 67–93.
4. Yun Liao, 2017. "Machine Learning in Macro-Economic Series Forecasting," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 9(12), pages 71-76, December.
5. Siami-Namini, Sima et al. “A Comparison of ARIMA and LSTM in Forecasting Time Series.” 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) (2018): 1394-1401.
6. Nonlinear Inflation Forecasting with Recurrent Neural Networks Anna Almosova Niek Andresen (2019)
7. Hakimov, Hakimjon. 2020. Issues of optimic basis of public financing in following financial and monetary policy. https://journal.tsue.uz/index.php/archive/article/view/927
8. Ҳакимов, Ҳакимжон. 2019. «Макроиқтисодий барқарорликни таъминлашда фискал ва монетар сиёсатни мувофиқлаштириш зарурати». https://journal.tsue.uz/index.php/archive/article/view/627
9. Махмудов, Носир; Ҳакимов, Ҳакимжон. 2017. «Макроиқтисодий барқарорликни мустаҳкамлаш- барқарор иқтисодий ўсишни таъминлашнинг муҳим шарти». https://journal.tsue.uz/index.php/archive/article/view/674.
10. Баҳриддинов, В.; Ҳакимов, Ҳ. (2018). Инфляцияни таргетлаш сиёсатини амалга оширишнинг хориж тажрибаси. https://journal.tsue.uz/index.php/archive/article/view/666
11. Ҳакимов, Ҳакимжон. 2018. «Мамлакатда фискал ва монетар сиёсатни мувофиқлаштиришнинг моҳияти ва сабаблари». https://journal.tsue.uz/index.php/archive/article/view/224.
12. Махмудов Н.М, Ҳакимов Ҳ.А. "Иқтисодий ўсиш моделлари ва улардан макроиқтисодий таҳлилда фойдаланиш йўналишлари." Экономика и финансы (Узбекистан) 3 (2016).
13. Хакимов, Х. А. (2016). Роль государства в обеспечении макроэкономической стабилности. Ученый XXI века, (5-2)
14. Ҳакимов, Ҳ. (2020). Инқироз ва ундан кейинги шароитлар учун давлат қарзларининг юқори чегараси. https://journal.tsue.uz/index.php/archive/article/view/1038
15. Баҳриддинов, В. (2019). Инфляцион таргетлаш сиёсатинининг хусусиятлари ҳамда уни ўзбекистонда жорий этиш истиқболлари. https://journal.tsue.uz/index.php/archive/article/view/1079