SURXONDARYO VILOYATIDA QISHLOQ XO'JALIGI MAHSULOTINI EVIEWS YORDAMIDA KO'P OMILLI MODELLASHTIRISH VA PROGNOZLASH

INTERNATIONAL CONFERENCE ON MODERN DEVELOPMENT OF PEDAGOGY AND LINGUISTICS Volume 02, Issue 10, 2025 18 INTERNATIONAL CONFERENCE ON MODERN DEVELOPMENT OF PEDAGOGY AND LINGUISTICS universalconference.us SURXONDARYO VILOYATIDA QISHLOQ XO'JALIGI MAHSULOTINI EVIEWS YORDAMIDA KO'P OMILLI MODELLASHTIRISH VA PROGNOZLASH Muallif: Yulduz Normamatova Annotatsiya Mazkur maqolada Surxondaryo viloyatida 2010–2021 yillar oralig'ida qishloq xo'jaligi mahsuloti hajmi (Q) kapital (K), mehnat (L) va yer (S) ko'rsatkichlari ta'sirida EViews dasturi yordamida ko'p omilli logarifmik model asosida tahlil qilindi. Model quyidagi tenglama bo‘yicha baholandi va 2022–2026 yillar uchun prognozlar chiqarildi. ln Q = 0.6984191 ln K + 4.143181 ln L - 1.612479 ln S - 11.51768Model natijalari Model natijalari EViews dasturida baholandi. Quyidagi asosiy ko‘rsatkichlar olingan: - LnK koeffitsienti: 0.6984191 - LnL koeffitsienti: 4.143181 - LnS koeffitsienti: -1.612479 - Konstanta: -11.51768 - R² = 0.99572 - F-statistic = 620.3746 - DW = 2.05384 INTERNATIONAL CONFERENCE ON MODERN DEVELOPMENT OF PEDAGOGY AND LINGUISTICS Volume 02, Issue 10, 2025 19 INTERNATIONAL CONFERENCE ON MODERN DEVELOPMENT OF PEDAGOGY AND LINGUISTICS universalconference.us Natijalar grafiklari 1-rasm. Surxondaryo: 2010–2021 yillar uchun haqiqiy va model bo‘yicha hisoblangan qiymatlar. 2-rasm. Surxondaryo: 2010–2026 yillar uchun haqiqiy va prognoz qiymatlar. EViews kodi ' EViews command file to reproduce the log-linear model and forecasts ' Data should be in a workfile with freq=annual 2010-2026 (or 2010-2021 for estimation) wfcreate(wf=surxondaryo) a 2010 2026 INTERNATIONAL CONFERENCE ON MODERN DEVELOPMENT OF PEDAGOGY AND LINGUISTICS Volume 02, Issue 10, 2025 20 INTERNATIONAL CONFERENCE ON MODERN DEVELOPMENT OF PEDAGOGY AND LINGUISTICS universalconference.us ' Read in historical data (2010-2021) - user can paste values manually or import CSV ' Example: series Q = 2841.1, 4264.9, 5283.6, 6575.4, 7992.4, 9796.6, 11873.4, 15290.9, 19606.3, 23686.7, 24689.7, 32030.4 ' Replace the following with your import command, e.g. read(t=csv) "/mnt/data/surxondaryo_agri_model/historical_data.csv" ' Log-transform variables series lnQ = log(Q) series lnK = log(K) series lnL = log(L) series lnS = log(S) ' Estimate log-linear model (OLS) equation eq1.ls lnQ c lnK lnL lnS ' Show regression output show eq1.output ' Generate fitted values and residuals series lnQ_hat = eq1.@fitted series Q_hat = exp(lnQ_hat) series resid = lnQ - lnQ_hat ' Forecasting (example: using provided trend models for K,L,S) ' Create series for K_trend, L_trend, S_trend for 2022-2026 ' K_trend = 874.9*t - 1889 (t = year) ' L_trend = 2.09*t + 315.86 ' S_trend = -2.167*t + 237.45 ' In EViews you can set series values for specific sample period: smpl 2022 2026 series t = @year series K_trend = 874.9*t - 1889 series L_trend = 2.09*t + 315.86 series S_trend = -2.167*t + 237.45 ' Compute ln values and model-predicted lnQ and Q for forecast period series lnK_tr = log(K_trend) series lnL_tr = log(L_trend) series lnS_tr = log(S_trend) series lnQ_fore = 0.6984191*lnK_tr + 4.143181*lnL_tr + -1.612479*lnS_tr + - 11.51768 series Q_fore = exp(lnQ_fore) ' Show forecasted series show Q_fore Sana: 2025-11-02