Modelling and Forecasting of Tea Production, Consumption and Export in Bangladesh
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Abstract
Bangladesh is the world’s 9th largest tea producer and the tea industry is a major contributor to the country’s economy. In order to provide information about the demand, supply and foreign trade of tea in the future, forecasting plays a vital role in adjusting the gaps and formulating policy. Taking all of this into account, this study aims at modelling and forecasting tea production, consumption and export in Bangladesh using ARIMA modelling for the period of 2019 to 2028. Forty-seven years of time-series data from 1972 to 2018 were obtained from the Bangladesh Tea Board. Forecasts were computed on the basis of models that were selected using three important information criteria such as Akaike's Information Criterion (AIC), Schwarz's Bayesian Information Criterion (BIC) and Correction for Akaike's Information Criterion (AIC). The study identified that the best-fitting models were ARIMA (0, 1, 0), ARIMA (0, 2, 2) and ARIMA (1, 1, 2) for tea production, consumption and export, respectively. Forecasting showed an upward trend for tea production from 83.40 to 94.88 million kg and consumption from 94.35 to 131.71 million kg over the period of 2019 to 2028. On the contrary, the forecast for tea exports shows a decreasing trend. Such forecast results indicate that the government should immediately take action to accelerate the growth of the tea industry in Bangladesh. Otherwise, the economic development of the country will be hampered by reduced export earnings while relying on imports to meet the domestic demand.
Keywords: tea production; tea consumption; tea export; ARIMA Model; forecasting; Bangladesh
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E-mail: anlaboni@bsmrau.edu.bd
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References
Rahman, A., 2017. Modeling of tea production in Bangladesh using Autoregressive Integrated Moving Average (ARIMA) Model. Journal of Applied & Computational Mathematics, 6(2), 349-357.
Hossain, M.M. and Abdulla, F., 2015. Forecasting the tea production of Bangladesh: Application of ARIMA model. Jordan Journal of Mathematics and Statistics, 8(3), 257-270.
Nasir, T. and Shamsuddoha, M., 2011. Tea productions, consumptions and exports: Bangladesh perspective. International Journal of Educational Research and Technology, 2(1), 68-73.
Wang, H., Wen, Y., Du, Y., Yan, X., Guo, H., Rycroft, J., Boon, N., Kovas, E. and Mela, D., 2010. Effects of catechin enriched green tea on body composition. Obesity, 18, 773-79.
Dhekale, B.S., Sahu, P.K., Vishwajith, K.P., Mishra, P. and Noman, M., 2014. Modelling and forecasting of tea production in West Bengal. Journal of Crop and Weed, 10, 94-103.
Ahammed, K.M., 2012. Investment for sustainable development of Bangladesh tea industry – An empirical study. [online] Available at: https://bea-bd.org/site/images/pdf/9.pdf.
Bangladesh Tea Board, 2019. Statistical Bulletin of Bangladesh Tea Board for the Month of November 2019. [online] Available at : http://teaboard.portal.gov.bd.
Milad, M., 2020. Tea Brews Booming Business in Bangladesh. [online] Available at: https://en.prothomalo.com/bangladesh/good-day-bangladesh/tea-brews-booming-business-in-bangladesh.
Raza, S.M.S., 2019. Prospects and challenges of tea industry in Bangladesh. The Cost and Management, 47(2), 119-135.
Bangladesh Bureau of Statistics (BBS), 2018. Statistical Yearbook of Bangladesh 2018. Ministry of Planning, Government of the People’s Republic of Bangladesh.
Baten, A., Kamil, A.A. and Haque M.A., 2002. Productive efficiency of tea industry: A stochastic frontier approach. African Journal of Biotechnology, 9, 3808-3816.
Shilpi, F.J., 1990. Estimating Income and Price Elasticities of Imports and Exports of Bangladesh. Research Report No. 122. Dhaka: Bangladesh Institute of Development Studies.
Das, N.C., 2006. Export demand function for Bangladesh’s tea. Bangladesh Journal of Agricultural Economics, 29(1-2), 1-18.
Sobuj, M.A., 2016. Assessing Socio-economic Status of Tea Garden Workers in Sylhet District. M.S. Thesis, Department of Agricultural Extension, Sher-e-Bangla Agricultural University, Dhaka, Bangladesh.
Islam, M.A., Sumy, M.S.A., Uddin, M.A. and Hossain, M.S., 2020. Fitting ARIMA model and forecasting for the tea production, and internal consumption of tea (per year) and export of tea. International Journal of Material and Mathematical Sciences, 2(1), 8-15.
Muhammed, F., Siddique, M., Bashir, M. and Ahamed, S., 1992. Forecasting rice production in Pakistan using ARIMA models. Journal of Animal and Plant Science, 2, 27-31.
Hossain, M.J., Samad, Q.A. and Ali, M.Z., 2006. ARIMA model and forecasting with three types of pulse prices in Bangladesh: a case study. International Journal of Social Economics, 33(3-4), 344-353.
Awal, M.A. and Siddique, M.A.B., 2011. Rice production in Bangladesh employing by ARIMA model. Bangladesh Journal of Agricultural Research, 1, 51-62.
Debnath, M.K., Bera, K. and Mishra, P., 2013. Forecasting area, production and yield of cotton in India using ARIMA model. Research & Reviews: Journal of Space Science & Technology, 2(1), 16-20.
Prabakaran, K., Sivapragasam, C., Jeevapriya, C. and Narmatha, A., 2013. Forecasting cultivated areas and production of wheat in India using ARIMA model. Golden Research Thoughts, 3(3), 1-7.
Box, G.E.P. and Jenkin, G.M., 1976. Time Series of Analysis: Forecasting and Control. Revised ed. San Francisco: Holden-Day.
RStudio Team, 2020. RStudio: Integrated Development for R. [online] Availabel at: http://www.rstudio.com/.
Shabbir, S.M. and Sa’adat, N., 2010. Expansion of tea production and export from Bangladesh: some policy suggestion. Thoughts on Economics, 16(3-4), 66-67.
Anderson, T.W., 1971. The Statistical Analysis of Time Series. New York: John Wiley.
Gujarati, D.N., and Porter, D.C., 2008. Basic Econometrics. 5th ed. Boston: McGraw Hill.
Dickey, D.A. and Fuller, W.A., 1979. Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(6), 427-431.
Kwiatkowski, D., Phillips, P.C.B., Schmidt, P. and Shin, Y., 1992. Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 54(1-3), 159-178.
Rahman, N.M.F. and Baten, M.A., 2016. Forecasting area and production of black gram pulse in Bangladesh using ARIMA models. Pakistan Journal of Agricultural Science, 53(4), 759-765.
Makridakis, S.G., Wheelwright, S.C. and Hyndman, R.J., 1998. Forecasting: Methods and Applications. 3rd ed. New York: Wiley.
Akaike, H., 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723.
Schwarz, G.E., 1978. Estimating the dimension of a model. Annals of Statistics, 6(2), 461-464.
Cavanaugh, J.E., 1997. Unifying the derivations of the Akaike and corrected Akaike information criteria. Statistics & Probability Letters, 31(2), 201-208.
Burnham, K.P. and Anderson, D.R., 2004. Multimodel inference: understanding AIC and BIC in model selection. Sociological Methods & Research, 33, 261-304.
Ljung, G.M. and Box, G.E.P., 1978. On a measure of a lack of fit in time series models. Biometrika, 65(2), 297-303.
McLeod, A.I. and Li, W.K., 1983. Diagnostic checking ARMA time series models using squared-residual autocorrelations. Journal of Time Series Analysis, 4, 269-273.
Walters, A.A., 1970. An Introduction to Econometrics. New York: W.W. Norton.
Makridakis, S., 1993. Accuracy measures: theoretical and practical concerns. International Journal of Forecasting, 9(4), 527-529.