INFLYATSIYA DARAJASI VA ISTE’MOL SAVATI QIYMATI O‘RTASIDAGI STATISTIK BOG‘LIQLIK: KORRELYATSION–REGRESSIYA YONDASHUVI
Keywords:
inflyatsiya, CPI, iste’mol savati, minimal iste’mol to‘plami, korrelyatsiya, regressiya, elastiklik, lag, Newey–West, mavsumiylik, tariflar, vaqt qatori.Abstract
Mazkur maqolada inflyatsiya darajasi bilan iste’mol savati (minimal iste’mol to‘plami) qiymati o‘rtasidagi statistik bog‘liqlik korrelyatsiya va regressiya yondashuvlari orqali baholanadi. Inflyatsiya narxlar umumiy darajasining o‘sishi bo‘lsa, iste’mol savati aholi muntazam xarid qiladigan tovar va xizmatlar majmuasining puldagi ifodasidir; demak, savat qiymatidagi o‘zgarish inflyatsiyaning kundalik xarajatlardagi aksini beradi. Tadqiqotda oyma-oy vaqt qatorima’lumotlari asosida iste’mol savati qiymati indekslanadi, inflyatsiya esa CPI o‘sish sur’ati sifatida olinadi.Dastlab Pearson va Spearman koeffitsiyentlari yordamida bog‘liqlik yo‘nalishi va kuchi aniqlanadi, so‘ng log-differensial regressiya modeli orqali savat qiymatining o‘zgarishi inflyatsiyaga qanday “o‘tishini” baholanadi. Avtokorrelyatsiya va geteroskedastiklikka chidamli baholash (Newey–West) hamda kechikmalar (lag) ta’sirini
tekshirish natijalarning barqarorligini oshiradi. Natijalar iste’mol savati qiymati inflyatsiya dinamikasiga yuqori sezgir ekanini, ayniqsa oziq-ovqat ulushi katta bo‘lgan davrlarda bog‘liqlik kuchayishini ko‘rsatadi. Hisob-kitoblar metodikani ko‘rsatish uchun namunaviy ma’lumotlar to‘plamida beriladi. Cheklov sifatida savat tarkibining yangilanishi, mavsumiylik va tariflar ta’siri kelgusi izlanishlarda alohida modellashtirilishi lozim. Amaliy jihatdan ushbu yondashuv ijtimoiy himoya choralarini (nafaqalar, ish haqi indeksatsiyasi) rejalashda, shuningdek, pul-kredit siyosati kommunikatsiyasida “xalq sezadigan inflyatsiya”ni tushuntirishda foydalidir. Natijalar savat og‘irliklari qayta ko‘rib chiqilganda bog‘liqlik koeffitsiyentlari ham o‘zgarishini ko‘rsatib, statistik monitoringda metodologik izchillik zarurligini ta’kidlaydi.
References
1.O‘zbekiston Respublikasi Prezidenti huzuridagi Statistika agentligi. Consumer Price Index (CPI) in the Republic of Uzbekistan (press release, October 2025): axborot byulleteni. — 2025. Statistika
2. O‘zbekiston Respublikasi Prezidenti huzuridagi Statistika agentligi. Consumer price index: metodologik metadata (Consumer-price-index.pdf). — 2021. Statistika
3. O‘zbekiston Respublikasi Prezidenti huzuridagi Statistika agentligi. On the cost of minimum consumer spending: rasmiy xabar. — 2025. Statistika
4. O‘zbekiston Respublikasi Prezidenti huzuridagi Statistika agentligi. On the cost of minimum consumer spending: rasmiy xabar. — 2024. Statistika
5. O‘zbekiston Respublikasi Markaziy banki. Year-on-year, month-on-month and cumulative inflation: inflyatsiya ko‘rsatkichlari (rasmiy sahifa). — 2025. CBU
6. O‘zbekiston Respublikasi Markaziy banki. Monetary Policy Report (2025 Q1): hisoboti. — 2025. CBU
7. International Monetary Fund (IMF). DSBB / e-GDDS: Uzbekistan — CPI (CPI00) metadata. — 2024. Dissemination Standards Bulletin Board
8. UN ESCAP. Modernization of the Consumer Price Index in Uzbekistan: taqdimot materiali. — 2025. ESCAP
9. Newey W. K., West K. D. A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix // Econometrica. — 1987. — Vol. 55. — P. 703–708. Social Science Computing Cooperative 10. Engle R. F., Granger C. W. J. Co-integration and Error Correction: Representation, Estimation, and Testing // Econometrica. — 1987. — Vol. 55(2). — P. 251–276. JSTOR
11. Dickey D. A., Fuller W. A. Distribution of the Estimators for Autoregressive Time Series with a Unit Root // Journal of the American Statistical Association. — 1979. — Vol. 74. — P. 427–431. Tandfonline
12. Said S. E., Dickey D. A. Testing for Unit Roots in Autoregressive-Moving Average Models of Unknown Order // Biometrika. — 1984. — Vol. 71(3). — P. 599–607. OUP Academic
13. Granger C. W. J. Investigating Causal Relations by Econometric Models and Cross-Spectral Methods // Econometrica. — 1969. — Vol. 37(3). — P. 424–438. IDEAS/RePEc
14. Ljung G. M., Box G. E. P. On a Measure of Lack of Fit in Time Series Models // Biometrika. — 1978. — Vol. 65(2). — P. 297–303. OUP Academic 15. Akaike H. A New Look at the Statistical Model Identification // IEEE Transactions on Automatic Control. — 1974. — Vol. 19(6). — P. 716–723. SCIRP
16. Schwarz G. Estimating the Dimension of a Model // The Annals of Statistics. — 1978. — Vol. 6(2). — P. 461–464. Statistical Sites
17. Box G. E. P., Jenkins G. M. Time Series Analysis: Forecasting and Control. — San Francisco: Holden-Day. — 1970. SCIRP
18. Hamilton J. D. Time Series Analysis. — Princeton: Princeton University Press. — 1994.









