پیش‌بینی تقاضای گوشت قرمز در ایران(کاربرد الگوی (ARIMAX

فصلنامه علمی -پژوهشی تحقیقات اقتصاد کشاورزی

مقاله 10، دوره 4، شماره 14، خرداد و تیر 1391، صفحه 175-190   

نویسندگان

رضا مقدسی^{* 1}؛ رباب محسن پور²

¹دانشیار گروه اقتصاد کشاورزی دانشگاه آزاد اسلامی واحد علوم و تحقیقات تهران

²دانشجوی دکترای اقتصاد کشاورزی دانشگاه آزاد اسلامی واحد علوم و تحقیقات تهران

تاریخ دریافت: 26 آذر 1394،  تاریخ پذیرش: 26 آذر 1394 

چکیده

گوشت یکی از مهم ترین منابع تأمین پروتئین است که برای سرمایه گذاری و برنامه ریزی در جهت تولید آن نیاز به پیش بینی تقاضای مصرف گوشت در آینده می باشد. لذا در این مقاله تلاش گردید تا با استفاده از داده‌های فصلی برای دوره ی 1386.4-1367.1 عملکرد دو الگوی ARIMA و ARIMAX به منظور پیش بینی تقاضای سرانه گوشت قرمز مورد مقایسه قرار گیرد. نتایج نشان داد که پیش بینی حاصل از فرآیند ARIMAX دقیق تر می باشد. لذا به کارگیری همزمان اطلاعات مصرف سرانه ی گوشت قرمز و برخی متغیرهای تأثیرگذار بر آن به افزایش دقت پیش بینی کمک می نماید.
 

کلیدواژه‌ها

پیش‌بینی؛ گوشت قرمز؛ فرآیند ARIMAX

عنوان مقاله [English]

Red Meat Demand Forecasting in Iran

نویسندگان [English]

R. Moghaddasi¹؛ R. Mohsenpour²

چکیده [English]

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

کلیدواژه‌ها [English]

Forecasting, ARIMAX, ARMA, Red meat. Demand

مراجع

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.