Application of Quantile Regression in the Analysis of the Fluctuations in the Price of Chicken meat in Iran

Subject Areas : Agricultural Economics Research

elham pourmokhtar ¹ (Department Islamic Azad University, Science and Research Branch, Tehran, Iran)
Reza Moghaddasi ² (Department Islamic Azad University, Science and Research Branch, Tehran, Iran)
Amir Mohammadi Nejad ³ (Department Islamic Azad University, Science and Research Branch, Tehran, Iran)
Seyed Safdar Hosseini ⁴ (Department of Agricultural Economics, University of Tehran, Iran)

Received: 2017-06-22 Accepted : 2021-10-11 Published : 2022-01-21

Keywords: Quantile Regression, Chicken Meat, price fluctuations,

Abstract :

Introduction: Short-term chicken supply (for example, over a year) lacks the flexibility to respond to demand fluctuations, which can negatively impact the welfare of consumers and producers. Thus, using various methods to identify these fluctuations can be useful in lowering or adjusting the market's price and equilibrium.
Materials and Methods: Using quantile regression and monthly data (April 2002 to March 2015), this study examined how short-term chicken price fluctuations in the Iranian market responded to related cost factors.
Findings: The results demonstrated that the confidence intervals generated by the quantile regression between quantiles 0.5th and 0.95th can indicate the range of possible price fluctuations. The economic values forecasted by this model not only help to analyze cost-effective factors and improve forecasting accuracy, but they also benefit from risk management in the agricultural market.
Conclusion: Among the cost factors influencing chicken prices, day-old chick (DOC) and consumer price index (CPI) had the greatest influence on price fluctuations. Coherent hatching planning based on seasonal needs, as well as price insurance for day-old chicks and a reduction in government interventions at certain times of the year, can all help to reduce price fluctuations.

References:

1. Balcilar M, Chang S, Gupta R, Kasongo V, Kyei C.  The Relationship between Oil and       Agricultural Commodity Prices: A Quantile Causality Approach. Working Papers, Department of Economics, Faculty of Economic and Management 2014; 50(3): 48-68.

https://econpapers.repec.org/paper/prewpaper/201468.htm

 

2. Bergolo M, Carbajal F. Exploring The Urban-Rural Labor Income Gap in Uruguay: A Quantile Regression Decomposition. Revista de Evista Aalisis Economico. 2010; 25(2):133-136.

 [DOI: 10.4067/S0718-88702010000200006].

 

3. Chidmi B, Solis D, Cabrera V. Analyzing the Sources of Technical Efficiency among Heterogeneous Dairy Farms: A Quantile Regression Approach. Prod. 2011; 13(2): 99107.

https://animalproduction.net/index.php/JAP/article/view/315

 

4. Dashti SE, Mohammadi H. Forecasting of chicken and egg prices using artificial neural network in Iran. Journal of Economic Research and Policies. 2010; 18(55):86-106.

http://qjerp.ir/article-1-231-fa.html

5. Mehrabani Yeghane H. Association of one-day-old chicken producers Poultry industry. 2015; 3(1):1-152. http://PRESS.Iran.http://www.itpnews.com/home/show/301/28096


6. Hosseini SS, Nikokar A, Dorandish A. Price Transmission Analysis in Iran Chicken Market.

International Journal of Agricultural Management and Development. 2012; 2(4):243-253.

https://www.sid.ir/en/journal/ViewPaper.aspx?id=295373

 

7. Kaditi E, Nitsi E. Applying regression Quantiles to farm efficiency estimation Agricultural and Applied Economics Association Annual Meeting. 2010; 25-27. https://www.aaea.org/meetings

 

8. Koenker R, Bassett G, Regression quantiles. Econometrica. 1978; 46(1): 33-50.

 [DOI: 10.2307/1913643]

 

9. Kostov P, Davidova S. A quantile regression analysis of the effect of farmers’ attitudes and perceptions on market participation. J. Agric. 2012; 64 (1): 112-32.

[DOI: 10.1111/j.1477-9552.2012.00366.x]

 

10. Ansari MT, Bameni Maghadam M, Khoshghoyan fard AR, Samaran E. Application of quantile regression in mental health analysis.Social welfare. 2006; 5(20):44-56

https://www.sid.ir/en/journal/ViewPaper.aspx?id=50322

 

11. Gan-qiong LI, Shi-wei X, Zhe-min L, Yi-guo S, Xiao-xia D. Using Quantile Regression Approach to Analyze Price Movements of Agricultural Products in China. Journal of Integrative Agriculture. 2012; 11(4): 674-83.

[DOI: 10.1016/S2095-3119(12)60055-0].