Application of ARIMA Model to Forecast Corn Prices in Mexico
DOI:
https://doi.org/10.55965/setp.4.08.a3Keywords:
ARIMA model, price forecast, corn prices, agricultural prices, MexicoAbstract
Corn is an essential grain in the Mexican culinary, cultural and social heritage. However, the volatility in corn prices brings uncertainty to agricultural farmers and has caused an increase in imports of the grain from other countries. The purpose of this study is to use time-series models regularly applied in finance to an agricultural commodity and forecast corn prices in Mexico. The study employs autoregressive integrated moving average (ARIMA) models to forecast prices in 2024 and 2025 using data on average rural prices of grain corn from the period 1980 to 2023 The results contribute to a theoretical discussion on employing statistical tools to reduce market uncertainty on agricultural commodities and provide empirical practical results on corn prices for decision making. The results are innovative in using the ARIMA statistical tool to analyze a specific commodity (corn) in a specific market (Mexico). The conclusions of the study suggest an upward trend in corn prices for 2024 and 2025, however, price stagnation and uncertainty is observed. Although government policies have introduced price guarantees for corn in Mexico, they only cover less than 3% of total production. Future studies should analyze price divergence by regions or states in Mexico.
Downloads
References
Agbo, H.M.S. (2023). Forecasting agricultural price volatility of some export crops in Egypt using ARIMA/GARCH model. Review of Economics and Political Science, 8(2),123-133. https://doi.org/10.1108/REPS-06-2022-0035 DOI: https://doi.org/10.1108/REPS-06-2022-0035
Banco Mundial (2024). PIB, UMN a precios constantes - Mexico. Retrieved February 18, 2024, from: https://datos.bancomundial.org/indicator/NY.GDP.MKTP.KN?locations=MX
Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time Series Analysis Forecasting and Control (5th ed.). Wiley. DOI:10.1111/jtsa.12194 DOI: https://doi.org/10.1111/jtsa.12194
Bezabih, G., Wale, M., Satheesh, N, Fanta, S. W. and Atlabachew, M. (2023). Forecasting cereal crops production using time series analysis in Ethiopia. Journal of the Saudi Society of Agricultural Sciences, 22(2), 546-559.
https://doi.org/10.1016/j.jssas.2023.07.001 DOI: https://doi.org/10.1016/j.jssas.2023.07.001
Diario Oficial de la Federación (DOF, 2019). Plan Nacional de Desarrollo. Retrieved July 12 2024, from:
https://www.dof.gob.mx/nota_detalle.php?codigo=5565599&fecha=12/07/2019#gsc.tab=0
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366a), 427–431. https://doi.org/10.2307/2286348 DOI: https://doi.org/10.1080/01621459.1979.10482531
Food and Agriculture Organization (FAO, 2021). OECD-FAO Agricultural Outlook
-2030. OECD and Food and Agriculture Organization of the United Nations. https://doi.org/10.1787/19428846-en DOI: https://doi.org/10.1787/19428846-en
Guimond-Ramos, J.C., Borbon-Morales, C.G., & Mejia-Trejo, J. (2023). Variations in the
expenditure of Mexican households on foods with a high energy content, 2016-2020. Scientia et PRAXIS, 3(05), 1-25.
https://doi.org/10.55965/setp.3.coed1.a1 DOI: https://doi.org/10.55965/setp.3.coed1.a1
Instituto Nacional de Geografía y Estadistica (INEGI, 2024). Banco de Información Económica
Bank. Retrieved March 17, 2024, from:
https://www.inegi.org.mx/app/indicadores/?tm=0#bodydataExplorer
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, 981-992.
http://jast.modares.ac.ir/article-23-2638-en.html
López-García, M.R., Martínez-Damián, M. A., & Arana-Coronado, J. J. (2021). Predicción del Maíz en México. Agrociencia, 55(8), 733-746.
https://doi.org/10.47163/agrociencia.v55i8.2665 DOI: https://doi.org/10.47163/agrociencia.v55i8.2665
Lobell, D. B., Schlenker, W., & Costa-Roberts, J. (2011). Climate Trends and Global Crop Production Since 1980. Science, 333(6042), 616-620.
DOI: 10.1126/science.1204531 DOI: https://doi.org/10.1126/science.1204531
Maiga, Y. (2024). Temporal Forecast of Maize Production in Tanzania: An Autoregressive
Integrated Moving Average Approach. Journal of Agricultural Studies, 12(2), 118-131. https://doi.org/10.5296/jas.v12i2.21679 DOI: https://doi.org/10.5296/jas.v12i2.21679
Martínez-Damián, M.A. & Brambila-Paz, J.J. (2023). Modeling of nominal vs real price
predictors applied to corn, wheat, and barley in Mexico. Revista Mexicana de Ciencias Agrícolas, 14(2), 295-301.
https://doi.org/10.29312/remexca.v14i2.2933 DOI: https://doi.org/10.29312/remexca.v14i2.2933
Morales, R. (2023, February 7). Mexico increases dependence on imported corn. El Economista. Retrieved April 11, 2024 from:
Pardey, P. G., Alston, J. M., & Chan-Kang, C. (2013). Public Agricultural R&D over the past half-century: An emerging new world order. Agricultural Economics, 44(s1), 103-113. https://doi.org/10.1111/agec.12055 DOI: https://doi.org/10.1111/agec.12055
Secretaría de Agricultura y Desarrollo Rural (SADER, 2021). Agri-food Regions of
Mexico. Retrieved February 28, 2024, from:
https://www.gob.mx/agricultura/articulos/regiones-agroalimentarias-de-mexico?idiom=es
Seguridad Alimentaria Mexicana (SAM, 2022). Guaranteed prices program for basic food products. Retrieved March 13, 2024, from:
Sistema de Información Agroalimentaria de Consulta (SIACON, 2024). Servicio de Información Agroalimentaria y Pesquera. Retrieved February 8, 2024, from:
https://www.gob.mx/siap/documentos/siacon-ng-161430
Thistleton, W. y Sadigov, T. (2023). Practical Time Series Analysis. Coursera.
https://www.coursera.org/learn/practical-time-series-analysis
U.S. Department of Agriculture (USDA, 2023). World Agricultural Supply and Demand
Estimates. Retrieved August 8, 2024, from:
United Nations (UN, 2015). Transforming our world: The 2030 agenda for sustainable
development. Retrieved August 18, 204, from: https://sustainabledevelopment.un.org/post2015/transformingourworld
Valdez-Galvez, M.J., Coronado-Gonzalez, Y.U.K. & Camarena-Gomez, B.O. (2023).
Environmental degradation and sustainability in areas with intensive agricultural practices of Sonora, Mexico. Scientia et PRAXIS, 3(05), 26-50. https://doi.org/10.55965/setp.3.coed1.a2 DOI: https://doi.org/10.55965/setp.3.coed1.a2
Yadav, S., Mishra, P., Kumari, B., Shah, I.A., Karakaya, K., Shrivastri, S., Fatih, C., Ray, S. & Khatib, A.M.G.A. (2022). Modelling and Forecasting of Maize Production in South Asian Countries. Economic Affairs, 67(04), 519-531.
DOI: 10.46852/0424-2513.4.2022.18 DOI: https://doi.org/10.46852/0424-2513.4.2022.18
Yasmin, S., & Moniruzzaman, Md. (2024). Forecasting of area, production, and yield of jute in
Bangladesh using Box-Jenkins ARIMA model. Journal of Agriculture and Food Research, 16, 1-14.
https://doi.org/10.1016/j.jafr.2024.101203 DOI: https://doi.org/10.1016/j.jafr.2024.101203
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 LEO GUZMÁN-ANAYA
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.