Open AccessAUTOREGRESSIVE PROCESSES AND GRAIN YIELD FORECASTING IN THE NORTHERN KAZAKHSTAN
Author(s) -
Talgat Kussaiynov,
Ainur Bulasheva,
Zh. O. Zhakupova
Publication year - 2018
Publication title -
innovacii i prodovolʹstvennaâ bezopasnostʹ
Language(s) - English
Resource type - Journals
ISSN - 2311-0651
DOI - 10.31677/2311-0651-2018-0-4-98-105
Subject(s) - autoregressive model , econometrics , variable (mathematics) , yield (engineering) , dispersion (optics) , agriculture , series (stratigraphy) , time series , mathematics , economics , statistics , geography , geology , mathematical analysis , paleontology , materials science , physics , archaeology , optics , metallurgy
Time series models are one of the most commonly used forecasting tools in the agricultural economy. In this case, the future values of the variable are function of the past values of the same variable. In other words, there are autoregressive processes. The dynamic of grain yields in the North-Kazakhstan and Kostanay regions of Kazakhstan demonstrate very similar statistical properties. In both cases, there is a positive linear trend, the cyclical development of the process is clearly discernible. Serious attention should also be given to the existence of a cycle in the dynamics of the dispersion level of crop yields. These stochastic features of the indicator should be taken into account in agricultural forecasting.