International Journal of Pure & Applied Bioscience (IJPAB)
Year : 2017, Volume : 5, Issue : 5
First page : (341) Last page : (347)
Article doi: http://dx.doi.org/10.18782/2320-7051.5409
Forecasting of Area and Production of Cotton in India: An Application of ARIMA Model
Vijaya B. Wali1*, Devendra Beeraladinni2 and Lokesh H3.
1,2Assistant Professor,3 Professor
Department of Agricultural Economics, UAS, Raichur-584104
*Corresponding Author E-mail: vbw06@rediffmail.com
Received: 8.08.2017 | Revised: 12.09.2017 | Accepted: 16.09.2017
ABSTRACT
The paper attempts forecasting of the area and production of cotton in India using the univariate autoregressive integrated moving average (ARIMA) model. The time series data on area and production of cotton in India for the period of 65 years from 1950-51 to 2015-16 was analyzed for the study. The best models were selected by comparing Akaike Information Criterion (AIC), Schwartz’s Bayesian Criterion (SBC), Normalized BIC; Mean Absolute Percentage Error (MAPE) and maximum values of R2. The study revealed that ARIMA (0, 1, 0) and ARIMA (1, 1, 1) were the best fitted models for forecasting area and production of cotton in India respectively. Selected models were used to forecast area and production of cotton for four years from 2017-18 to 2020-21. The analysis showed an increasing trend in area and production of cotton. If the present trend continues, the cotton area in India in the year 2020-21 would be 123.83 lakh ha with upper and lower limits of 146.51 and 101.16 lakh ha, respectively and the production of cotton would be 316.16 lakh bales (170 kg each) with upper and lower limits of 412.70 and 219.63 lakh bales, respectively.
Key words: Area, production, Autoregressive integrated moving average (ARIMA) model, Box and Jenkins, Forecasting.
Full Text : PDF; Journal doi : http://dx.doi.org/10.18782
Cite this article: Wali, V.B., Beeraladinni, D. and Lokesh, H., Forecasting of Area and Production of Cotton in India: An Application of ARIMA Model, Int. J. Pure App. Biosci.5(5): 341-347 (2017). doi: http://dx.doi.org/10.18782/2320-7051.5409
