International Research Journal of Agricultural Economics and Statistics

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

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
     

   Subscribe/Renew Journal


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.
Subscription Login to verify subscription
User
Notifications
Font Size


  • Box, G.E.P., Jenkins, G.M. and Reinsel, G.C. (2007). Time series analysis: forecasting and control, 3rd Ed. Dorling Kindersley, Pvt. Ltd., New Delhi (India).

  • Contreras, J., Espinola, R., Nogales, F.J. and Conejo, A.J. (2003). ARIMA models to predict nextday electricity prices, IEEE Transactions on Power Systems, 18 (3): 1014- 1020.

  • Meyler, Aidan, Kenny, Geoff and Quinn, Terry (1998). Forecasting Irish inflation using ARIMA models, Central Bank and Financial Services Authority of Ireland Technical Paper Series, Vol. 1998, No. 3/RT/98, pp. 1-48.

  • Prajneshu and Venugopalan, R. (1998). On nonlinear procedure for obtaining length - weight relationship. Indian J. Anim. Sci., 68 (1) : 452-456.

  • Singh, S., Ramasubramanian,V. and Mehta, S.C. (2007). Statistical models for forecasting rice production of India. J. Indian Society of Agric. Statist., 61(2): 80- 83.

  • Stergiou, K. I. (1989), Modeling and forecasting the fishery for pilchard (Sardina pilchardus) in Greek waters using ARIMA time-series models, ICES J. Marine Sci., 46 (1):16-23.


Abstract Views: 492

PDF Views: 0




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

Abstract Views: 492  |  PDF Views: 0

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