The use of a univariate time series model to short term forecast the behaviour of beef production in Baja California, México

Main Article Content

Alberto Barreras Serrano
Eduardo Sánchez López
Fernando Figueroa Saavedra
José Ángel Olivas Valdez
Cristina Pérez Linares


The Box-Jenkins methodology was used to select an ARMA model to forecast beef production in Baja California, Mexico. The series of bovine carcasses processed monthly in the state's slaughterhouses between 2003 and 2010 was used. Because the inspection of the series graph and correlogram did not determine a stationary behavior, an augmented Dickey-Fuller test was performed and it was found that the series was stationary. As a result of identifcation procedure, an AR (1) and an ARMA (2, 1) models were selected and estimated using ordinary least squares. The estimated models were compared using the signifcance of the regression coeffcient and the Akaike information and Schwartz Bayesian criteria. A diagnostic check was done examining the goodness of ft of the models by plotting the residuals; the Q statistic was used to test for autocorrelation. Because the results were similar, a predictive effcacy evaluation of two models was carried out using a group of forecast error statistics. The result of these tests indicated that the ARMA (2,1) had a better forecasting capability, this was supported by plotting together a forecasted series with the actual series and the out-of sample prediction for January of 2011. The results support the use of ARMA models to obtain reliable short term forecasts of beef production in Baja California.

Keywords: beef, time series, ARMA, forecasting

Article Details


1. Delgado EJ, Rubio MS, Iturbe FA, Mendez RD, Cassis RD, Rosiles R. Composition and quality of Mexican and imported retail beef in Mexico. Meat Sci 2005; 69: 465-471.

2. SIAP 2011. Avance mensual de la producción pecuaria por Estado 2010. Sistema de información agroalimentaria y pesquera. [Serie en línea: 2011 abril] [Citado: 2011 octubre 20] Disponible en: URL: http://

3. Sanders DR, Manfredo MR. USDA production forecasts for pork, beef and broilers: An evaluation. J Agric R Econ 2002; 27:114-127.

4. Myers RJ, Sexton RJ, Tomek WG. A century of research on agricultural markets. Am J Agric Econ 2010; 92:376 -402.

5. Allen PG. Economic forecasting in agriculture. Int J Forecasting 1994;10:81-135.

6. Evans MK. Practical Business forecasting. Malden Massachussets: Blackwell Publishers, 2003.

7. Asteriou D, Stephen GH. Applied econometrics: A modern approach. New York USA: Palgrave MacMillan, 2007.

8. Vogelvang B. ECONOMETRICS. Theory and applications with Eviews. Essex, UK: Pearson Education Limited, 2005.

9. Box GEP, GM Jenkins. Time series analysis, fore- casting and control. Oakland, California: Holdan Day, 1976.

10. Harris R, Sollis R. Applied time series modeling and forecasting. West Sussex UK: John Wiley & Sons, 2005.

11. Kennedy P. A guide to econometrics. 4th ed. Cambridge Massachusetts: MIT press, 1998.

12. Enders W. Applied econometrics Time Series. Hoboken New Jersey: Wiley, 2004.

13. Gujarati, DN, Porter DC. Econometría. 5ª ed. México DF: Mac Graw Hill, 2010.

14. Ngurah AIG. Time series data analysis using eviews. Singapore: John Wiley & Sons, 2009.

15. Pindyck RS, Rubinfeld DL. Econometría modelos y pronósticos. 4ta ed. México DF: Mc Graw-Hill, 2001.

16. Quantitative Micro Software. Eviews 6. Irvine, California, 2007.

17. Cuenca JNJ, Chavarro MF, Díaz GOH. El sector de ganadería bovina en Colombia. Aplicación de modelos de series de tiempo al inventario ganadero. Rev Fac Cienc Económicas. 2008; 16: 165-177.

18. Martínez VC. Modelos de pronóstico de la producción bovina. Archivos de economía. Documento 269. Departamento Nacional de Planeación Dirección de Estudios Económicos. Bogotá, Colombia, 2004

19. Dickey DA, Bell RW, Miller RB. Unit root in time series models: Test and implication. The Am Statiscian 1986; 40: 12-26.

20. Brooks C. Introductory Econometrics for Finance. 2nd ed. Cambridge, Massachusetts: Cambridge University Press, 2008.

21. Koheler AB, Mumphree ES. A comparison of the Akaike and Schwarz criteria for selecting model order. App Stat 1988; 37:187-195.

22. Nelson BK. Time series analysis using autoregressive integrated moving average (ARIMA) models. Aca Emer Med 1998;5:739-744

PLUMX Metrics