Price analysis and forecasting for decision making: Insights from wheat markets in India

A G Adeeth Cariappa; Babita Kathayat; S Karthiga; R Sendhil

doi:10.56093/ijas.v90i5.104376

Authors

A G Adeeth Cariappa ICAR-National Dairy Research Institute, Karnal, Haryana 132 001, India
Babita Kathayat ICAR-National Dairy Research Institute, Karnal, Haryana 132 001, India
S Karthiga ICAR-National Dairy Research Institute, Karnal, Haryana 132 001, India
R Sendhil ICAR-National Dairy Research Institute, Karnal, Haryana 132 001, India

https://doi.org/10.56093/ijas.v90i5.104376

Keywords:

ARIMA, Forecasting, Seasonal index, Wheat, Wholesale prices

Abstract

Wheat occupies a prime position in supplementing the food security needs of India. Price forecast related to a food commodity is essential in executing policies which ensure market support. Keeping this in view, an attempt was made to forecast monthly wholesale wheat prices adopting ARIMA model in spatially separated markets of India using the historical data sourced from AGMARK price portal (July 2002-June 2018). Wheat prices exhibited a clearcut seasonality captured through monthly price indices. The prices were found to be highest during the crop season (November-March) as it is the production phase lacking market supply and lowest during post-harvest season (June- October) wherein supply surge is witnessed. The average seasonal price variation and intra-year price rise were found to be highest in Haryana, followed by Punjab. Forecasted prices estimated by fitting the ARIMA model were found to be higher for low or negligible wheat producing states such as Kerala and Karnataka, and lower for higher wheat producing states like Haryana, Punjab, Madhya Pradesh and Uttar Pradesh. Forecast performance the fitted models were further supported by using measures like RMSE, MAPE and MAE with 95% confidence interval. The study emphasized the need for effective dissemination of market information such as price forecast to farmers, agri-based industries and other concerned stakeholders which will help in decision making apart from tracking price volatility.

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