International Research Journal of Agricultural Economics and Statistics

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ARIMA Model for Forecasting of Greengram Prices in Telangana by using SAS


Affiliations
1 Department of Agricultural Economics (A.M.I.C.), College of Agriculture, Jayashankar Telangana State Agricultural University, Hyderabad (Telangana), India
2 Department of Agricultural Economics (A.M.I.C.), College of Agriculture, Professor Jayashankar Telangana State Agricultural University, Hyderabad (Telangana), India
     

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Autoregressive integrated moving average (ARIMA) approach has been applied for modeling and forecasting of greengram prices in Telangana. Autocorrelation (AC) and partial autocorrelation (PAC) functions were estimated, which led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting the future production. To this end, evaluation of forecasting is carried out with Akaike’s information criterion (AIC) and Schwarz’s Bayesian information criterion ( BIC). The best identified model for the data under consideration was used for out-of-sample forecasting upto November 2019.

Keywords

ARIMA Model, Forecasting, Greengram, SAS.
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  • ARIMA Model for Forecasting of Greengram Prices in Telangana by using SAS

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Authors

R. Vijaya Kumari
Department of Agricultural Economics (A.M.I.C.), College of Agriculture, Jayashankar Telangana State Agricultural University, Hyderabad (Telangana), India
G. Ramakrishna
Department of Agricultural Economics (A.M.I.C.), College of Agriculture, Professor Jayashankar Telangana State Agricultural University, Hyderabad (Telangana), India
Panasa Venkatesh
Department of Agricultural Economics (A.M.I.C.), College of Agriculture, Professor Jayashankar Telangana State Agricultural University, Hyderabad (Telangana), India
A. Sreenivas
Department of Agricultural Economics (A.M.I.C.), College of Agriculture, Professor Jayashankar Telangana State Agricultural University, Hyderabad (Telangana), India

Abstract


Autoregressive integrated moving average (ARIMA) approach has been applied for modeling and forecasting of greengram prices in Telangana. Autocorrelation (AC) and partial autocorrelation (PAC) functions were estimated, which led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting the future production. To this end, evaluation of forecasting is carried out with Akaike’s information criterion (AIC) and Schwarz’s Bayesian information criterion ( BIC). The best identified model for the data under consideration was used for out-of-sample forecasting upto November 2019.

Keywords


ARIMA Model, Forecasting, Greengram, SAS.

References