Analysis of Price Behavior in Sri Lankan Vegetable Market

Author(s): Pasdunkorale Arachchige Jayamini Champika, Amin Mugera

DOI: doi.org/10.56527/fama.jabm.10.1.2

Keywords: Vegetable prices; Sri Lanka; Structural breaks; ARIMA; GARCH  

                    
Abstract: Vegetables are important source of nutrient for the Sri Lankan population and both farmers and consumers are adversely affected by vegetable price volatility. The lack of price analysis and forecasting has made it difficult to establish an effective early warning system for the vegetable farming sector in Sri Lanka. This study investigates the price behaviour of selected fresh vegetables - carrot, cabbage, and tomato - and forecasts the future prices and volatilities using time series techniques. Analysis of weekly price data from 1997 to 2018 revealed that all three - price series had one structural break, but none coincided with the policy change when the government introduced fertilizer subsidies for vegetable producers in the agriculture sector. The autoregressive integrated moving average (ARIMA) model estimations show that the best model for forecasting carrot price is ARIMA (3,1,2) (0,0,2)[52]*  capable of predicting retail prices at 71% accuracy while the best model for cabbage prices is ARIMA(1,1,1)(0,0,1)[52] with a prediction accuracy of 55%. All three-price series exhibit serial correlation in residuals; hence GARCH estimations were used to model and predict volatility. Of the fitted ARMA GARCH models, the best model for estimating the volatility of carrot and cabbage were GARCH (1, 2) ARMA (3, 2) and GARCH (1,1) ARMA (3 ,2), respectively. The volatility predictions for the first ten weeks for the year 2019 indicate a gradual decrease in volatility in the carrot price series whilst a gradual increase in volatility in the cabbage price series.

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References
 

Department of Agriculture. (2019). Agricultural Statistics - vol xvi. Department of Agriculture, Sri Lanka. Retrieved from https://doa.gov.lk/SEPC/images/PDF/AgStat.pdf

Department of Census and Statistics. (2019). Food balance sheet 2013-2017.  Department of Census and Statistics, Sri Lanka. Retrieved from http://www.statistics.gov.lk/agriculture/FoodBalanceSheet/FBS_Report2013-2017.pdf

Dessus, S., Herrera, S., & De Hoyos, R. (2008). The impact of food inflation on urban poverty and its monetary cost: some back‐of‐the‐envelope calculations. Agricultural Economics, 39, 417-429.

Engle, R. (2001). GARCH 101: The use of ARCH/GARCH models in applied econometrics. Journal of Economic Perspectives, 15(4), 157–168. doi:10.1257/jep.15.4.157

Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 987-1007. 

Eni, D., & Adeyeye, F. (2015). Seasonal ARIMA modeling and forecasting of rainfall in Warri town, Nigeria. Journal of Geoscience and Environment Protection, 3, 91-98. doi: 10.4236/gep.2015.36015.

FAO., & OECD. (2011). Price volatility in food and agricultural markets. Policy responses. Report including contributions by FAO, IFAD, IMF, OECD, UNCTAD, WFP, the World Bank, the WTO, IFPRI and the UN HLTF. Retrieved from http://www.fao.org/fileadmin/templates/est/Volatility/Interagency_Report_to_the_G20_on_Food_Price_Volatility.pdf

Filter, K. (2017). GARCH – Modelling conditional variance & useful diagnostic tests. LOGICAL ERRORS - A Blog About Practical Problem Solving & Quantitative Analysis. Retrieved from https://logicalerrors.wordpress.com/2017/08/14/garch-modeling-conditional-variance-useful-diagnostic-tests/#comments.

Gujarati, D. N., & Porter, D. (2009). Basic econometrics. Mc Graw-Hill International Edition.

Hathurusingha, C., Abdelhamid, N., & Airehrour, D. (2019). Forecasting models based on data analytics for predicting rice price volatility: A case study of the Sri Lankan rice market. Journal of Information and Knowledge Management, 18(1), 1–20. doi:10.1142/S0219649219500060

Henegedara, M. G. (2016). Price instability and change of terms of trade in small farming sector in Sri Lanka with special reference to cultivation of paddy and vegetables. International Journal of Development Research. 6(7), 8757-8761.

Hossain, M. Z., Samad, Q. A., & 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(4), 344–353. doi:10.1108/03068290610651652

Illankoon, I. M. G. L., & Kumara, B. T. G. S. (2020). Analyzing the Influence of Various Factors for Vegetable Price using Data Mining. 13th International Research Conference. General Sir John Kotelawala Defence University. Retrieved from http://ir.kdu.ac.lk/bitstream/handle/345/3029/FOC%20402-409.pdf?sequence=1&isAllowed=y

Ivanisevic, D., Mutavdzic, B., Novkovic, N., & Vukelic, N. (2015). Analysis and prediction of tomato price in Serbia. Ekonomika Poljoprivrede, 62(4), 951–962. doi:10.5937/ekopolj1504951i

Jadhav, V., Chinnappa Reddy, B. V., & Gaddi, G. M. (2017). Application of ARIMA model for forecasting agricultural prices. Journal of Agricultural Science and Technology, 19(5), 981–992. Retrived from https://jast.modares.ac.ir/article-23-2638-en.html

Jayawardena, R., Byrne, N. M., Soares, M. J., Katulanda, P., & Hills, A. P. (2013). Food consumption of Sri Lankan adults: An appraisal of serving characteristics. Public Health Nutrition, 16(4), 653–658. doi:10.1017/S1368980012003011

Kamu, A., Ahamed, A., & & Yusoff, R. (2008). Short-term forecasting models of Bagan Datoh Cocoa Bean Price. [Paper presentation]. In Applied International Business Conference -2008, Malaysia. Retrieved from https://www.researchgate.net/publication/291975077_Short-Term_Forecasting_Models_of_Bagan_Datoh_Cocoa_Bean_Price/link/56a9daed08ae2df821658182/download

Komarek, A. M., De Pinto, A., & Smith, V. H. (2020). A review of types of risks in agriculture: What we know and what we need to know. Agricultural Systems, 178, 102738.

Kim, J. H., & Choi, I. (2017). Unit roots in economic and financial time series: a re-evaluation at the decision-based significance levels. Econometrics, 5(3), 41. doi:10.3390/econometrics5030041

Kwiatkowski, D., Phillips, P. C., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? Journal of econometrics, 54(1–3).

Lama, A., Jha, G.K., Paul, R.K., & Gurung, B. (2015). Modelling and forecasting of price volatility: an application of GARCH and EGARCH models §. Agricultural Economics Research Review, 28(1), pp.73-82 doi: 10.5958/0974-0279.2015.00005.1

Lee, C. C., & Chang, C. P. (2005). Structural breaks, energy consumption, and economic growth revisited: evidence from Taiwan. Energy Economics, 27(6), 857-872. doi:10.1016/j.eneco.2005.08.003
Ling, T.Y., Nor, A.H.S.M., Saud, N.A. & Ahmad, Z. (2013). Testing for unit roots and structural breaks: Evidence from selected ASEAN macroeconomic time series. International Journal of Trade, Economics and Finance, 4(4), 230-237

Manero, A. (2017). Income inequality within smallholder irrigation schemes in Sub-Saharan Africa. International Journal of Water Resources Development, 33(5), 770-787. doi:10.1080/07900627.2016.1152461

Marambe B., Silva P., & Athauda, S. (2017). Agriculture and rural development under central government and provincial council setup in Sri Lanka. In: Cooray N., & Abeyratne S. (eds) Decentralization and development of Sri Lanka within a unitary state. Springer, Singapore. doi:10.1007/978-981-10-4259-16

Ministry of Agriculture, Rural Economic Affairs, Irrigation, and Fisheries, & Aquatic Resources Development. (2019). Sri Lanka overarching agricultural policy (Draft). Ministry of Agriculture, Rural Economic Affairs, Irrigation, and Fisheries, & Aquatic Resources Development. Retrieved from http://www.agrimin.gov.lk/web/index.php/en/downloads/policy 
                      
Ministry of Agriculture. (2012). Market based food production plan 2013-2016. Ministry of Agriculture  Sri Lanka. Retrieved from http://www.agrimin.gov.lk/web/index.php/en/downloads/report-articles

Ministry of Health, Nutrition Division. (2011). Food based dietary guidelines for Sri Lankans - 2011 (2nd edition). A Publication by Nutrition Division Ministry of Health. Retrieved from http://www.fao.org/3/a-as886e.pdf

Mitra, D., & Paul, R. K. (2017). Hybrid time-series models for forecasting agricultural commodity prices. Model Assisted Statistics and Applications, 12(3), 255–264. doi:10.3233/MAS-170400

Mitra, S., & Boussard, J. M. (2012). A Simple model of endogenous agricultural commodity price fluctuations with storage. SSRN Electronic Journal, 10458(August). doi:10.2139/ssrn.1908115

Montañés, A., & Reyes, M. (2000). Structural breaks, unit roots and methods for removing the autocorrelation pattern. Statistics and Probability Letters, 48(4), 401–409. doi:10.1016/S0167-7152(00)00023-7

Moumoniz, Z., & Kamdem, J. S. (2016). Agricultural production decision using jumps and seasonal volatility in commodity prices. ResearchGate-online. doi: 10.13140/RG.2.2.28258.12486

Mugera, A. W., Curwen, R., & White, B. (2016). Deregulation of the Australian wheat export market: What happened to wheat prices? Journal of International Food and Agribusiness Marketing, 28(1), 18–34. doi:10.1080/08974438.2014.940125

Muhimbula, H., Kinabo, J., & O'Sullivan, A. (2019). Determinants of infant nutrition status in rural farming households before and after harvest. Maternal & Child Nutrition, 15(3), e12811.

Myers, R. J. (1994). Time series econometrics and commodity price analysis: a review. Review of Marketing and Agricultural Economics, 62(430-2016-31314), 167-181.

Nelson, D.B. (1991). Conditional heteroscedasticity in asset returns: A new approach. Econometrica, 59: 347-370. doi: 10.2307/2938260

Perera, K. B. P., Herath, H. M. L. K., Jayasinghe-Mudalige, U. K., Edirisinghe, J. C., Udugama, J. M. M., & Wijesuriya, W. (2016). Price analysis of selected marine fish available in Colombo fish markets. Sri Lanka Journal of Economic Research, 4(1), 19. doi:10.4038/sljer.v4i1.69

Perron, P., & Vogelsang, T. (1992). Nonstationary and level shifts with an application to purchasing power parity. Journal of Business & Economic Statistics, 10(3), 301-320. doi:10.2307/1391544

Purohit, S. K., Panigrahi, S., Sethy, P. K., & Behera, S. K. (2021). Time series forecasting of price of agricultural products using hybrid methods. Applied Artificial Intelligence, 35(15), 1388-1406.

Rathnayake, R. M. Y. L., Razmy, A. M., & Alibuhtto, M. C. (2016). A comparison of price fluctuations for the green chilli and tomato in Dambulla dedicated economic centre in Sri Lanka. Proceedings of Fifth Annual Science Research Sessions 2016 on "Enriching the Novel Scientific Research for the Development of the Nation" pp.144-156. Faculty of Applied Sciences, South Eastern University of Sri Lanka

Reddy, A. A. (2019). Price forecasting of tomatoes. International Journal of Vegetable Science, 25(2), 176–184. doi: 10.1080/19315260.2018.1495674

Reddy, A.A., S.S. Raju, A. Suresh, and P. Kumar. (2018). Analysis of pearl millet market structure and value chain in India. Journal of Agribusiness in Developing and Emerging Economies. 8(2):406–424. doi:10.1108/JADEE-02-2016-0007

Salvi, J. (2019). Significance of ACF and PACF plots in time series analysis. Towards Data Science. Retrieved from https://towardsdatascience.com/significance-of-acf-and-pacf-plots-in-time-series-analysis-2fa11a5d10a8

Sendhil, R., Amit, K., Mathur, V. C., & Jha, G. K. (2014). Price volatility in agricultural commodity futures-an application of GARCH model. Journal of the Indian Society of Agricultural Statistics, 68(3), 365-375.

Shumway, R. H., & Stoffer, D. S. (2011). Time series and its applications: With R examples. Springer Science, New York. Retrieved from https://web.njit.edu/~wguo/Math447/Time%20Series%20Analysis%20and%20Its%20Application%20with%20R%20examples.pdf

Taylor, J.E., & Adelman, I. (2003). Agricultural household models: genesis, evolution, and extensions. Review of Economics of the Household, 1(1-2),33-58.

Wang, D., & Tomek, W. G. (2007). Commodity prices and unit root tests. American Journal of Agricultural Economics, 89(4), 873–889.  doi:10.1111/j.1467-8276.2007.01031.x

Wickramasinghe, Y. M., & Pradeep, G. S. (2014). Price behavior of selected upcountry vegetables in Sri Lanka. Journal of Accountancy & Finance. Retrieved from http://repository.wyb.ac.lk/bitstream/handle/1/1978/JOURNAL-OF-ACCOUNTANCY-AND-FINANCE-2014-59.pdf?sequence=2

Wennström, A. (2014). Volatility forecasting performance: Evaluation of GARCH type volatility models on Nordic equity indices. (Unpublished master's thesis). Department of Mathematics. Royal Institute of Technology. Stockholm, Sweden. Retrieved from https://www.diva-portal.org/smash/get/diva2:725958/FULLTEXT01.pdf

World Bank. (2019). Arable land (% of land area) - Sri Lanka. The World Bank Data. Retrieved from https://data.worldbank.org/indicator/AG.LND.ARBL.ZS?locations=LK

World Bank. (2020a). Poverty overview.  Retrieved from  https://www.worldbank.org/en/topic/poverty/overview

World Bank. (2020b). Sri Lanka vegetable exports by region 2017. World Integrated Trade Solutions. Retrieved from https://wits.worldbank.org/CountryProfile/en/Country/LKA/Year/2017/TradeFlow/Export

Xu, X. (2020). Corn cash price forecasting. American Journal of Agricultural Economics, 102(4), 1297–1320. doi:10.1002/ajae.12041

Yang, J., Haigh, M.S. and Leatham, D.J. (2001). Agricultural liberalization policy and commodity price volatility: a GARCH application. Applied Economics Letters, 8(9), pp.593-598. doi: 10.1080/13504850010018734

Yang, L., & Hamori, S. (2018). Modeling the dynamics of international agricultural commodity prices: A comparison of GARCH and stochastic volatility models. Annals of Financial Economics, 13(03), 1850010.

Zhang, J. H., Kong, F. T., Wu, J. Z., Zhu, M. S., Xu, K., & Liu, J. J. (2014). Tomato prices time series prediction model based on wavelet neural network. Applied Mechanics and Materials, 644–650, 2636–2640. doi:10.4028/www.scientific.net/amm.644-650.2636

Zivot, E., & Andrews, D. W. K. (2002). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business & Economic Statistics, 20(1), 25–44 doi:10.1198/073500102753410372