Abstract :
Using annual data of urban red meat, broiler, and fish consumptions over 1974-2008 period, this study compares the forecast performance of vector error correction models with and without imposing the homogeneity restriction in the cointegration space. To this end, out of sample forecasts for both restricted and unrestricted models were generated and evaluated based on the Mean Square Error (MSE) criterion. The test of equality of forecast errors indicated that there was a significant difference between the forecast errors of restricted and unrestricted models. This suggests that imposition of homogeneity restriction tends to improve the forecast accuracy when the restriction is not rejected. JEL Classification : C01,C53, E27, Q19
References:
1. Azizi. J and Torkamani. J. 2001. Estimation of demand functionfor different types of meat in Iran: application of cointegration.Agricultural Economics and Development.34: 217-238.(inPersian)
2. Bessler. D and Wang. Z. 2002. The Homogeneity restriction andforecasting performance of VAR-type demand systems: Anempirical examination of US meat consumption. Journal ofForecasting. 21: 193–206
3. Bessler. D and Brant. J. 1979. Composite forecasting oflivestock prices: An analysis of combining alternative
forecasting method. Purdue University Agricultural ExperimentStation.
4. Borimnezhad. V and Shoushtarian. A. 2008. Analyzingsimultaneousness of supply and demand system equations ofmeats in Iran, Agriculture Economics journal, 1:65-86(inPersian)
5. Central Bank of Islamic Republic of Iran, Reports of retail priceindex of commodities and services in urban areas (differentyears). Tehran, Iran.
6. De Crombrugghe. D. Palm FC. and Urbain. J.F. 1997. Statisticaldemand functions for food in the USA and the Netherlands.Journal of Applied Econometrics, 12: 615–645.
7. Ghanbari. Adivi. A. 1996. Meat supply and demand model inIran. Thesis for degree of PhD in Agricultural Economics.
University of Tarbiat Modaress.
8. Ghorashi Abhar. S.J and Sadr Alashrafi. S.M. 2005. Estimationdemand for major types of meat in IRAN: using almost idealdemand system. Journal of Agricultural Sciences, 3: 133-143(in
Persian)
9. Ghorashi Abhari S.J and Borimnejad V. 2005. Estimation ofsimultaneous equation demand and supply system for meat inIran. Agricultural Economics and Development (Eqtesad-EKeshavarzi Va Towse’e ), 51: 65-86(in Persian)
10.10-Heien D.M Chen T.N and Garrido A. 1996. Empiricalmodels of meat demand: How do they fit out of sample.
Agribusiness, 12: 51–66.
11.Harvey D.I, Leybourne S.J and Newbold P. 1997. Testing theequality of prediction mean square errors. International Journalof Forecasting, 13: 281–291.
12.Jahangard H. 2008. Demand projection for major foodcommodities in Iran. Thesis for Degree of Ms.c in Agricultural
Economics. University of Tehran.
13.Johansen S. 1995. Likelihood-based inference in cointegratedvector autoregressive models. Oxford University Press.
14.Kastens T.L and Brester G.W. 1996. Model selection andability of theory-constrained food demand systems.
American Journal of Agricultural Economics, 78: 301–312.
15.Kirchgassner G and Wolters J. 2007. Introduction to moderntime series analysis. Springer Publication, New York.
16.Murphy B, Norwood B and Wohlgenant M. 2003. Do economicrestrictions improve forecasts? Paper presented at the annualsouthern agricultural economics association meeting, Alabama.
17.Samadi A.H. 2007. Meat demand analysis in Iranian urbanareas: An application of AIDS model. Agricultural Economicsand Development (Special Issue in Agricultural Markets), 57:31-60(in Persian)
18.Statistical Center of Iran, Households budgetary reports in urbanareas (different years). Tehran, Iran.
19.Wang Z and Bessler D.A. 2003. Forecast evaluation in meatdemand analysis. Agribusiness, 4: 505-524
20.Zaranezhad M and Saadatmehr M. 2007. Estimation of demandfunctions for meat in Iran. Journal of Humanities and SocialSciences, 26:22-34(in Persian)
