FORECASTING ARECA NUT MARKET PRICES USING THE ARIMA MODEL: A CASE STUDY OF INDIA

Abstract

India is the major producer of Areca nut in the world. Volatile demand and price are the major challenges for the Areca nut growers in India. The use of time series models to manage the price risk has become the interest of academicians today. This paper deals with developing an appropriate model to predict the prices of a new variety of Areca nut in Karnataka using monthly price data for the period January 2009 to December 2018. Box Jenkins ARIMA methodology is used to develop the model. Along with ARIMA estimates, log-likelihood, Akaike’s information criterion (AIC) and Bayesian (BIC) information criterion statistics are also estimated to decide on the appropriate model. ACF and PACF correlograms for residuals of ARIMA are used to do the diagnostic check of the selected ARIMA model. Appropriate model to forecast the new variety Areca nut price is ARIMA (3, 1, 3)

Keywords

• investment behavior
• areca nut
• price forecasting
• arima models
• volatility
• time series
• acf
• pacf
• stock market forecasting