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Vol. 11, Special Issue 4 (2022)

Forecasting food grains yield in Haryana: A time series approach

Author(s):
Pooja Rawat and Sanju
Abstract:
The present study focuses on the forecasting of food grains yield in Haryana using autoregressive integrated moving average (ARIMA) technique. The annual data of Haryana on food grains yield were divided into the training data set from 1966-67 to 2015-16 and the testing data set from 2016-17 to 2019-20. Box Jenkins approach was used for model identification, the best possible ARIMA model is fitted among various competing models. Goodness of fit of each of these models is tested and the best model is used to forecast the yield of food grains. It is inferred that ARIMA (0,1,1) was found to be optimal and that the forecast values for the years 2020-21 to 2023-24 were estimated on the basis of this model which were 4.035, 4.094, 4.154 and 4.213 million tonnes respectively. The performances of these models validated with the R2, RMSE, MAPE, MAPE and BIC. The forecasted values and percentage relative deviation comes within acceptable limits. Forecasted values are very useful for the policymakers and government agencies for proper policy decision regarding food security.
Pages: 230-235  |  301 Views  82 Downloads
How to cite this article:
Pooja Rawat and Sanju. Forecasting food grains yield in Haryana: A time series approach. The Pharma Innovation Journal. 2022; 11(4S): 230-235.

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