Assessing the Asymmetric Effect of Corn Price on Chicken Meat Price in Iran

Document Type : Research Article

Authors

• Z. Shokoohi
• M.H. Trazkar
• F. Nasrnia

Department of Agricultural Economics, Shiraz University, Shiraz, Iran

https://doi.org/10.22067/jead.2021.69872.1031

Abstract

Introduction: Studying Iran’s poultry sector shows that the feed costs account for a large portion of the total cost of poultry production. Besides, corn as the feed for poultry had the largest share of total feed cost. According to the governmental trade policy and exchange rate variability, corn prices fluctuate in Iranian market. However, the demand for chicken meat has increased in recent years. This is due to the relative increase in the price of red meat compared to chicken, as well as promoting the health benefits of consuming white meat (chicken and fish). However, the chicken meat market has been accompanied by price shocks and price increases, and these fluctuations are one of the main challenges of poultry industry in the country. Examining the cost of production inputs in the Iranian poultry industry shows that poultry feed costs, especially corn, accounts for the largest share.
Materials and Methods: In this study, the smooth transition autoregressive (STAR) model was used to investigate the threshold effect of corn price, as one of the most important inputs of poultry feed, on the price of chicken meat. This method is a nonlinear approach of time series analysis which evaluates the asymmetric changes in the pattern parameters with a smooth transition by considering one or more thresholds. Nonetheless, this model allows several regimes and the transition among them to examine the relationship among research variables which is more realistic than that obtained using the traditional linear regression model. To estimate the nonlinear STAR model, monthly data of corn and chicken meat prices from 1993 to 2020 were collected from the State Livestock Affairs Logistics (S.L.A.L). Several steps were performed to estimate the STAR model. First, data stationary was tested using the seasonal Hylleberg, Engle, Granger, and Yoo (HEGY) unit root test. After investigating the order of variables, the optimal number of lags was determined using the Akaike information criterion (AIC). Smooth threshold linearity versus nonlinearity test was then performed to ensure that the STAR method was appropriate. Then, the applicable forms of transfer function and transfer variable were determined. Finally, after estimating the model, the hypotheses of normality and non-autocorrelation of residuals were tested.
Results and Discussion: HEGY seasonal unit root test indicates that the logarithms of corn and chicken meat prices do not have seasonal and non-seasonal unit roots, and these data are stationary. Then, three lags are selected as the optimal number of lags using the AIC, and the first lag of chicken meat logarithm is determined as the best transition variable based on the minimum sum of squares of error. Besides, nonlinearity tests suggest that the Exponential Smooth Transition Auto-regression (ESTAR) specification with two-regime switching fits the data better. The empirical results imply that the real threshold value of chicken meat price is statistically significant and equal to 1028.6 Rails. According to the consumer price index in 2020, the nominal threshold value of chicken meat price is 28800 Rails. Therefore, 1% increase in the price of corn will increase chicken meat price by 0.4% and 1.2% in the lower and upper regimes, respectively, after three months.
Conclusion: In this study, the threshold effect of corn price as one of the most important inputs of poultry feed on the price of chicken meat was investigated using Smooth Transition Regression (STR) model. This study's results show that the effect of corn price on meat price is nonlinear and asymmetric. The asymmetry feature is revealed in three aspects: lagged impact, direct impact, and the strength of influence. Based on the results, it is suggested that the government adopt appropriate policies to establish the stability in corn price as one of the most important inputs of poultry industry via foreign exchange and trade policies. Thus, fluctuations in the price of chicken meat can be prevented. It is also expected that if the chicken meat price increases in one period, the price in the next period will also be affected by the relationship between the first lagged chicken meat prices and its current period prices in both regimes. Therefore, the government should prevent fluctuations in chicken meat prices to ensure food security. In this regard, in addition to implementing pricing policies affecting the price of poultry industry inputs, based on the results of the previous section, controlling the price of corn is the most important factor.
 

Keywords

• Corn price
• Chicken meat price
• Smooth transition autoregressive model

20.1001.1.20084722.1400.35.2.7.9

Main Subjects

• Marketing and Trade of Agriculture Production

References

• Abdi A., and Rezaei A. 2009. Predicting corn and soybean meal prices using neural-fuzzy network. 6^th Iranian Agricultural Economics Conference, Mashhad, Iran. (In Persian with English abstract)
• Alizadeh M., Golkhandan A., and Sahebeh Mohamadian M. 2015. Threshold effects of external debt on economic growth of Iran: smooth transition regression (STR) model. Quarterly Journal of Applied Theories of Economics 2(2): 1-24.
• Barboza G., Gavinelli L., Pede V., Mazzucchelli A., and Di Gregorio A. 2020. A contribution to the empirics of food price behavior: the case of rice price dynamics in Italy. British Food Journal 123(1): 1-22.
• Chen C. 2012. Analysis of the interactive relationship between the vertical market price of the hog industry in China. Price, Theory and Practice 1: 39–40.
• Dick V.D., Tersvirta T., and Franses P.H. 2002. Smooth transition autoregressive models: a survey of recent developments. Econometric Reviews 21(1): 1–47.
• Dick V.D., Tersvirta T., and Franses P.H. 1999. Modeling multiple regimes in the business cycle. Macroeconomic Dynamics 3(3): 311–340.
• Eitrheim Ø., and Teräsvirta T. 1996. Testing the adequacy of smooth transition autoregressive models. Journal of Econometrics 74(1): 59-75.
• Ghadami Kohestani, NikoukarA., and Dourandish A. 2011. Threshold Price Transmission Model in Iranian Chicken Market. Journal of Economics and Agricultural Development 24(3): 384. (In Persian with English abstract)
• Ghahremanzadeh M., Dashti GH., and Rassouli Birami 2016. Price Volatility and Conditional Correlation of Livestock and Poultry Vertical Market Levels in Iran: Using Constant and Time varying Conditional Correlation Models. Agricultural Economics 10(3): 19-46. (In Persian with English abstract)
• Gilanupour A., Kohansal M., Permeh Z., and Ismailipour A. 2012. Investigating the effect of government interventions in the chicken meat market. Business Research Journal 63: 168-137. (In Persian with English abstract)
• Hadian E., and Oji Mehr S. 2014. Investigating the behavior of foreign exchange market pressure index in Iran: using a smooth transition autoregressive model (STAR). Quarterly Journal of Applied Economics Studies in Iran 3(10): 247-266.
• Hassouneh I., Serra T., and Gil J.M. 2010. Price transmission in the Spanish bovine sector: the BSE effect. Agricultural Economics 41(1): 33-42.
• Holt M.T., and Craig L.A. 2006. Nonlinear dynamics and structural change in the US. Hog-corn cycle: a time-varying STAR approach. American Journal of Agricultural Economics 88(1): 215-233.
• Jafari M. 2018. Non-linear effects of economic globalization on income inequality in Iran: smooth transition regression (STR) model. Journal of Economic Growth and Development Research 8(29): 61-76. (In Persian with English abstract)
• Jafari Samimi A., Montazeri Shoorekchali J., and Tatar M. 2014. Life Expectancy and Economic Growth in Iran: Smooth Transition Regression (STR) Approach. Journal of Economic Growth and Development Research 4(13): 117-128. (In Persian with English abstract)
• Kim S.W., and Bhattacharya R. 2009. Regional housing prices in the USA: an empirical investigation of nonlinearity. The Journal of Real Estate Finance and Economics 38(4): 443-460.
• Li M., Yang J., and Xu Z. 2012. Impacts of feeding patterns on fluctuations and cycle of pork price: a comparison between china, the United States and Japan. Issues in Agricultural Economy, 12.
• Luukkonen , Saikkonen P., and Teräsvirta T. 1988. Testing linearity against smooth
  transition autoregressive models. Biometrika 75(3): 491–499.
• Martin V., HurnS., and Harris D. 2013. Econometric modelling with time series. Cambridge University Press, New York.
• Mashayekhi S., and Hajizadeh Fallah M. 2011. The study of factors affecting the chicken meat market in Iran (application of self-regression vector model). Economic Research Journal 11(1): 154-131. (In Persian with English abstract)
• Moghaddasi R., and Yousefi Moteghaed H. 2011. Price Discovery in Chicken Market: An Application of Directed Acyclic Graph. Journal of Agricultural Economics Researches 3(2): 79-98.
• Pishbahar E., Ferdosi R., and Assadollahpour F. 2019. Price Transmission of Chicken: Application of Vector Autoregressive Markov-Switching (MSVAR). Approach, Agricultural Economics 9(2): 57-72.
• Rassouli Birami Z., Ghahremanzadeh M., and Dashti Gh. 2012. Analysis of Price Integration in Iran's Broiler Market. Agricultural Economic and Development20(78): 200- 211. (In Persian with English abstract) 
• Skalin J., and Tera¨svirta T. 2002. Modeling Asymmetries and Moving Equilibria in Unemployment Rates. Macroeconomic Dynamics 6(2): 202–41.
• Teräsvirta T. 1994. Specification, estimation, and evaluation of smooth transition auto regression models. Journal of the American Statistical Association 89(425): 208–218.
• Vajdi F., Ghahremanzadeh M., and Hosseinzad J. 2018. Risk Spillover Effect of Exchange Rate on Chicken Market and its Major Inputs in Iran. Journal of Economics and Agricultural Development 32(3): 213-225. (In Persian with English abstract)
• Van Dijk D., Terasvirta T., and Franses P.H. 2002. Smooth transition autoregressive models - a survey of recent developments. Econometric Reviews 21(1): 1-47.
• Wang G.Y., Si R.X., Li C.X., Zhang G.T., and Zhu N.Y. 2018. Asymmetric price transmission effect of corn on hog: evidence from China. Agricultural Economics 64(4): 186–196.
• Wei J., and He P.M. 2013. Empirical research on the relation­ship between the pigs lets price and hog price fluctuation. Feed Industry 21: 60–64.
• Zhou J., and Chen L. 2014. Research on the asymmetric price transmission of pork industry chain in China based on threshold model. Inquiry into Economic 1: 127–134.