Abstract :
Meat is an important source of animal protein in Iranian diet. Therefore, accurate estimate of meat demand for future, could lead to more realistic planning of import. This paper aims to evaluate two common methods which applied in forecasting economic variables, namely ARIMA and ARIMAX to predict red meat demand using quarterly data over period of 1988-2005. Results showed better performance of ARIMAX method based on conventional criteria
References:
1. Azizi, J. and Torkamani, J.2001. Estimation of demand function
for different types of meat in Iran: Application of co-integration.
Agricultural Economics and Development. 34: 217-237.
2. Bierens H.J, 1987. ARMAX model specification testing with an
application to unemployment to the Netherlands. Journal of
Econometrics. 35: 90-161.
3. Bos CH.S., Franse P.H., and Ooms M. 2002. Inflation, forecast
interval and long memory regression models. International
Journal of Forecasting. 18: 243-246.
4. Daneshvar Kakhki, M., Dehghanian, S., Hatef, H. and Sorori, A.
2007. Investigating how to determine guaranteed price of wheat
and its prediction. Journal of Economics and Agricultural
Development. 21(2): 57-66.
5. Day R.H, 1965. Probability Distribution of Field Crop Yields.
Journal of Farm Economics. 47: 713-741.
6. Ezatabadi Abdolahi, M. 2002. Investigation of Income
fluctuation of Iranian pistachio producers. PhD Thesis , Shiraz
university.
7. Farajzade, Z. and Shahvali, O. 2009. Forecasting agricultural
products prices: case study of cotton, rice and saffron.
Agricultural Economics and Development. 67: 43-71.
8. Fidan H, Klasra A.M, 2005. Seasonality in Household Demand
for Meat and Fish: Evidence from an Urban Area. Turk Journal of
Vet Anim. Sci. 29: 1217-24.
9. Food science conference, 2005.How much meat do we eat?
Consumption statistics. India.
10. Franses H.P, Hobijn B, 1997. Critical Values for Unit Root Tests
in Seasonal Time Series. Journal of Applied Statistics. 24: 25-47.
11. Fullerton T.M, Cecilia Nava A, 2003. Short Term Water Dynamic
in Chihuahua City, Mexico. Journal of Water Resources
Research.39: 101-106.
12. Gallagher P, 1987. U.S. Soybean Yields: Estimation and
Forecasting with Nonsymmetrical Distribution. American Journal
of Agricultural Economic. 69: 796-803.
13. Ghorashi Abhari, S.J. and Sadrolashrafi, S.M. 2005. Estimation of
meat Demand in Iran using AIDS model. Agricultural Sciences
Journal. 11(3): 133-143.
14. Ghorashi Abhari, S.J. and and Borimnejad, V. 2005.
Simultaneous Estimation of demand and supply for meat in Iran.
Agricultural Economics and Development. 51:65-93.
15. Hylleberg S, Engle R.F, Granger C.W.J, Yoo B.S, 1990. Seasonal
Integration and Cointegration. Journal of Econometrics. 44(1-2):
215-238.
16. Li Ch, Chen J, 2009. Traffic Accident Macro Forecast Based on
ARIMAX Model. International Conference on Measuring
Technology and Techtronic Automation. 632-639.
17. Lin Y.S, Hildreth R.J, Tefertiller K.R, 1963. Non-parametric
Statistical Tests for Bunchiness of Dry Land Crop Yields. Journal
of Farm Economics. 45: 592-598.
18. Luttrel C.B.M, Gilbert R.A, 1976. Crop Yields: Random Cyclical
or Bunchy? American Journal of Agricultural Economics. 58:
521-531.
19. Ministry of Agriculture. 2007. Information and Statistics
Department , Deputy in Livestock Affairs.
20. Miran B, Akgungor S, 2005. The Effect of Meat Cow (BSE)
Scare on Demand and Sales loss: the Sase of Izmir. Turk Journal
of Vet Anim. Sci. 25: 225-31.
21. Najafi, B., Zibaei, M., Sheikhi, M.H. and Tarazkar, M.H. 2007.
Price forecasting for some agricultural products in Fars province:
Application of Artificial Neural Network. Journal of Science and
Technology of Agricultural and Natural Resource. 11(1):501-512.
22. Pulina M, 2003. Quantitative Forecasting for Tourism OLS and
ARIMAX Approaches. University of Sassari and Crenos.
23. Samadi, A.H. 2007. Analysis of meat demand in urban areas of
Iran by using an Almost Ideal Demand system (AIDS).
Agricultural Economic and Development. 57: 23-55.
24. Statistical Canter of Iran. 1971-2007. Registration statistics about
urban and rural households.
25. Tarazkar, M.H. Price forecasting for some agricultural products
in Fars province: Application of Artificial Neural Network.
Master of Science Thesis , Shiraz University.
26. Tayebi, S.K. Azerbaijani, K. and Bayari, L. 2009. Forecasting
egg price in Iran: a comparison between Artificial Neural
Networks and ARCH methods. Agricultural Economics and
Development. 65:73-97.
27. Tayebi, S.K. Azerbaijani, K. and Bayari, L. 2009. A comparison
between Artificial Neural Networks and Time series methods for
forecasting poultry price in Iran. Macroeconomic Research letter.
9: 59-78.
28. Vere D.T, Griffith G.R, 1990. Comparative Forecast Accuracy in
the New South Wales Prime Lamb Market. Australian Journal of
Agricultural Economics. 34(2): 103-117.
29. Wallis WA, Moore GH, 1941. A significance test for time series
analysis. J Am Stat Ass .20:257–64
30. Wang Z, Bessler DA, 2003. Forecast Evaluations in Meat
Demand Analysis. Agribusiness: An International Journal.
19(4):505-524.
31. Yaali Jahromi, M., Mohammadi, H. and Farajzade, Z. 2009.
Forecasting sugar beet price in Iran. Sugar Beet Journal. 25(1):
97-111.
32. Yeong-Sheng T, 2009. A Managerial Economist's Forecast For
meat Consumption in Malaysia: Implications to Farmers and
Investors. Institute of Agricultural and Food Policy Studies,
University Putra Malaysia, Malaysia. MPRA Paper No. 14810.
33. Zeranezhad, M. and Saadatmehr, M. 2007. Estimation of demand
for red meat in Iran. Journal of Humanities and Social Sciences.
7 (26): 63-82.
