Time Series Analysis of Monthly Coffee (Robusta) Prices in India using Box-Jenkins Approach
Prema Borkar*
Gokhale Institute of Politics and Economics (Deemed University), Pune, Maharashtra (411 004), India
DOI: https://doi.org/10.54083/ResBio/4.3.2022/156-160
Keywords: Autocorrelation function, Box-Jenkins Approach, Coffee Prices, Partial autocorrelation function, Residual Analysis
Abstract
Robusta coffee is a type of coffee made from the Coffea canephora plant's beans (seeds). It is the world's second most popular coffee, accounting for 43% of global coffee production with arabica constituting the remainder except for the 1.5% constituted by Coffea liberica. The purpose of this study is to focus on predicting monthly coffee prices in India by using the historic time series data. The objective of this paper is to fit an Autoregressive Integrated Moving Average model using Box-Jenkins approach. Numerous fields, including agricultural production, animal husbandry and dairy economics, stock price prediction, etc. depend heavily on forecasting. To choose the best model, Autoregressive (AR), Moving Average (MA), and Autoregressive Integrated Moving Average (ARIMA) processes was used to select the best model for monthly coffee prices in India. This paper discusses ARIMA (p, d, q) time series analysis and its components, ACF, PACF, Normalized BIC, Box-Ljung Q Statistics, and Residual analysis. According to the best fitted model i.e., ARIMA (0,2,1) monthly coffee prices in India is expected to increase to INR 89.35 kg-1 in the month of November 2022. The outcomes are represented numerically and graphically.
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Reference
Anonymous, 2016. Understanding the Difference: Arabica vs. Robusta. The Coffee Barrister. Available at: https://www.coffeeb.net/arabica-vs-robusta. Retrieved on: 2nd August 2016.
Anonymous, 2019. Coffee: World Markets and Trade (PDF). United States Department of Agriculture - Foreign Agricultural Service. December 2019. Available at: https://downloads.usda.library.cornell.edu/usdaesmis/files/m900nt40f/6m3129089/r494w654j/coffee.pdf. Retrieved on: 8th May, 2020.
Anonymous, 2022. Coffee Industry and Exports. Available at: https://www.ibef.org/exports/coffee-industry-in-india. Retrieved on: 10th July, 2022.
Assis, K., Amran, A., Remali, Y., Affendy, H., 2010. A comparison of univariate time series methods for forecasting cocoa bean prices. Trends in Agricultural Economics 3(4), 207-215. DOI: 10.3923/tae.2010.207.215.
Box, G.E.P., Jenkins, G.M., 1976. Time Series Analysis: Forecasting and Control, Revised Edition. Holden-Day, San Francisco, CA. ISBN: 978-0-8162-1104-3. p. 567.
Dagoon, J., 2005. Agriculture & Fishery Technology IV. Rex Bookstore, Inc., Manila, Phillippines. ISBN: 9789712342233. p. 58.
Horowitz, A.R., 2004. Insect Pest Management: Field and Protected Crops. Springer. ISBN: 978-3-540-20755-9. p. 41.
Nochai, R., Nochai, T., 2006. ARIMA model for forecasting oil palm price. In: Proceedings of the 2nd IMT-GT Regional Conference on Mathematics, Statistics and Applications. University Sains Malaysia, Penang, June 13-15, 2006. pp. 1-7.
Urgert, R., Katan, M.B., 1996. The cholesterol raising factor from coffee beans. Journal of the Royal Society of Medicine 89(11), 618-623.