Vol. 4 No. 7 (2026), Articles
Vol. 4 No. 7 (2026)

O‘ZBEKISTONDA INFLYATSIYA DINAMIKASINI EKONOMETRIK MODELLASHTIRISH VA VAQTLI QATORLAR ASOSIDA PROGNOZLASH

Articles
Published 2026-05-03
Xakimova Ma’mura Muxammadiyevna
Samarqand Iqtisodiyot va servis institut, “Oliy matematika” kafedrasi assistenti
Murodullayeva Rushana Otabek qizi
Samarqand iqtisodiyot va servis instituti Iqtisodiyot fakulteti talabasi
PDF

Keywords

inflyatsiya, vaqtli qatorlar, mavsumiy parchalash, prognozlashtirish, trend, O‘zbekiston iqtisodiyoti, narx darajasi.

Abstract

Ushbu maqolada O‘zbekistonda inflyatsiya darajasini vaqtli qatorlar usulida prognozlashtirish masalasi ko‘rib chiqiladi. So‘ngi besh yillik choraklik ma’lumotlari asosida mavsumiy parchalash usuli qo‘llanilgan, trend va mavsumiy tarkibiy qismlar aniqlangan hamda prognoz tayyorlangan. Tadqiqot natijalari inflyatsiyani oldindan bashorat qilish va makroiqtisodiy siyosatni rejalashtirish uchun ilmiy asos bo‘lib xizmat qilishi mumkin.

PDF

References

Al Rasasi, M., & Cabezon, E. (2022). Uzbekistan’s Transition to Inflation Targeting. IMF Working Papers, No. 2022/229. International Monetary Fund. https://www.imf.org/en/Publications/WP/Issues/2022/11/18/Uzbekistan-s-Transition-to-Inflation-Targeting-525745

2. IMF. (2024). Republic of Uzbekistan: 2024 Article IV Consultation — Press Release and Staff Report. IMF Country Report No. 24/210. https://www.elibrary.imf.org/view/journals/002/2024/210/article-A001-en.xml

3. Davlatov, E., & Sági, J. (2025). From plan to market price: inflation drivers in New Uzbekistan. Journal of “Public Finance Quarterly”, 71(3), 46–68. https://doi.org/10.35551/PFQ_2025_3_2

4. Corpin, S. J. T., Marbella, J. N. P., Kua, S. J. J., Mabborang, R. C., & Lamprea, C. T. (2023). Forecasting Inflation Rate in the Philippines Using Seasonal Autoregressive Integrated Moving Average (SARIMA) Model. European Journal of Computer Science and Information Technology, 11(2), 13–36. https://eajournals.org/ejcsit/vol11-issue-2-2023/forecasting-inflation-rate-in-the-philippines-using-seasonal-autoregressive-integrated-moving-average-sarima-model/

5. Arif, E., Herlinawati, E., Devianto, D., Yollanda, M., & Permana, D. (2024). Hybridization of Long Short-Term Memory Neural Network in Fractional Time Series Modeling of Inflation. Frontiers in Big Data, 6, 1282541. https://doi.org/10.3389/fdata.2023.1282541

6. IMF. (2023). World Economic Outlook, October 2023: Navigating Global Divergences. International Monetary Fund. https://www.imf.org/en/publications/weo/issues/2023/10/10/world-economic-outlook-october-2023

7. Predicting the Rate of Inflation in Uzbekistan Using Seasonal ARIMA Models. (2024). ResearchGate Publication No. 381021871. https://www.researchgate.net/publication/381021871_Predicting_the_Rate_of_Inflation_in_Uzbekistan_using_seasonal_ARIMA_models

8. Hassan, A., Alam, M. Md., & Faeique, A. (2023). Forecasting Monthly Inflation in Bangladesh: A Seasonal Autoregressive Moving Average (SARIMA) Approach. Journal of Economics and Financial Analysis, 7(2), 25–43. https://ideas.repec.org/a/jfr/ijfr11/v12y2021i3p70-77.html

9. Petropoulos, F., et al. (2022). Forecasting: Theory and Practice. International Journal of Forecasting, 38(3), 705–871. https://eprints.gla.ac.uk/258994/1/258994.pdf

10. O‘zbekiston Respublikasi Milliy Statistika Qo‘mitasi. Rasmiy statistik ma’lumotlar bazasi. https://stat.uz