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Forecasting Farm-gate Catfish Prices in Uganda Using Sarima Model

James Obadiah Bukenya

Abstract


Stabilization of prices of essential agricultural commodities continues to remain an area of major concern for policy makers; given that price instability affects both producers and consumers, and has macroeconomic implications. This paper examines farm-gate price behavior in the African catfish markets in Uganda, and develops a forecasting model that adjusts for the seasonal fluctuations in the price series. The analysis utilizes monthly catfish real price series for the period January 2006 to December 2013. The model provides good in-sample and out-of-sample forecasts for the eight-year time period. The out-sample predictions based on SARIMA (1, 1, 1) (0, 1, 1)12 model suggest that the stochastic seasonal fluctuations depicted in the price series are successfully modeled, and that catfish real prices follow an upward trend. The findings can assist policy makers and major stakeholders to gain insight into more appropriate economic and sectorial policies that can lead to the development of reliable market information systems and up-to-date data on catfish supply, demand and stocks.

Keywords


SARIMA models, Box – Jenkins, Forecasting, catfish prices, Uganda

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References


AMC. (2013). Aquaculture Management Consultant Ltd (Kampala: Uganda).

Box, G.E.P. & G.M. Jenkins (1970). Time series analysis; Forecasting and control. Holden-Day, San Francisco (CA).

Box, G. E. P. & G. M. Jenkins (1976). Time series analysis: Forecasting and control. Revised edition, San Francisco: Holden Day.

Bukenya, J.O., & M. Ssebisubi (2015). Price transmission and threshold behavior in the African catfish supply chain in Uganda.” Journal of African Business, 16(1-2), 180-197.

Bukenya, J.O., & M. Ssebisubi (2014). Price integration in the farmed and wild fish markets in Uganda. Fisheries Science, 80 (6), 1347-1358.

Bukenya, J.O. (2017). Assessment of price volatility in the fisheries sector in Uganda. Journal of Food Distribution Research, XLVIII (1), 81–88.

Curtis M. J., C. Ligeon & N. Hishamunda (1998). Forecasting catfish industry prices using linear and nonlinear methods. Aquaculture Economics & Management, 2(2), 71-80.

FAO/FishStat, 2016. National Aquaculture Sector overview: Uganda. Retrieved July 10, 2017, from http://www.fao.org/fishery/countrysector/naso_uganda/en.

Forsberg, O.I., & A.G. Guttormsen (2006). The value of information in salmon farming. Harvesting the right fish at the right time. Aquaculture Economics & Management, 10,183–200.

Franses, P. H. (1991). Seasonality, nonstationarity and the forecasting of monthly time series. International Journal of Forecasting, 7, 199–208.

Gordon, D.V. (2017). Price modelling in the Canadian fish supply chain with forecasts and simulations of the producer price of fish. Aquaculture Economics & Management, 21(1), 105-124.

Gu, G., & J.L. Anderson (1995). Deseasonalized state-space time-series forecasting with application to the U.S. salmon market. Marine Resource Economics, 10, 171–185.

Guttormsen, A. G. (1999). Forecasting weekly salmon prices: Risk management in fish farming. Aquaculture Economics and Management, 3 (2), 159–166.

Hylleberg, S., R.F. Engle, C.W.J Granger & B.S. Yoo (1990). Seasonal integration and cointegration. Journal of Econometrics, 44, 215–238.

Prista, N., N. Diawara, M.J Costa & C. Jones (2011). Use of SARIMA models to assess data-poor fisheries: A case study with a sciaenid fishery off Portugal. Fishery Bulletin, 109(2), 170-185.

UBoS. (2013). Uganda Bureau of Statistics. Statistical abstracts 1995-2013. Retrieved September 17, 2013, from http://www.ubos.org/index.php.

Xiaoshuan, Z., H. Tao, B. Revell & F. Zetian (2005). A forecasting support system for aquatic products price in China. Expert Systems with Applications, 28(1), 119-126.




DOI: http://dx.doi.org/10.18686/fm.v2i2.1047

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