Seasonality and Forecasting of Monthly Broiler Price in Iran
محورهای موضوعی : Environmental policy and management
آزاده فلسفیان
1
(استادیار، گروه مدیریت ، توسعه و آموزش کشاورزی، واحد تبریز، دانشگاه آزاد اسلامی، تبریز، ایران)
کلید واژه: seasonality, Broiler price, periodic autoregressive (PAR), Seasonal unit root, seasonal integrated,
چکیده مقاله :
The objective of this study was to model seasonal behavior of broiler price in Iran that can be used to forecast the monthly broiler prices. In this context, the periodic autoregressive (PAR), the seasonal integrated models, and the Box-Jenkins (SARIMA) models were used as the primary nominates for the forecasting model. It was shown that the PAR (q) model could not be considered as an appropriate method for modeling seasonal behavior of the broiler price. Results of seasonal unit root test indicated that the monthly prices of broiler follow a non-stationary stochastic seasonal process. Accordingly, the regression-based model is an appropriate modeling framework. While SARIMA is an alternative modeling approach, the RMSE of forecasting error suggested the superiority of the regression-based model over the SARIMA model. Therefore, the estimated parameters of the regression-based model can be used to predict the monthly prices of broiler in Iran.
هدف از مطالعه حاضر بررسی رفتار فصلی قیمت گوشت مرغ در ایران و پیش بینی مقادیر آتی می باشد. بدین منظور، از روشهای خودرگرسیونی دورهایی (PAR)، مدلهای جمعی فصلی و باکس-جنکینز فصلی (SARIMA) بهره گرفته شد. نتایج کار نشان داد رفتار فصلی قیمت گوشت مرغ از یک الگو دورهایی تبعیت نمینماید، لذا مدل PAR(q)، مدل مناسبی جهت الگوسازی رفتار فصلی قیمت گوشت مرغ در ایران نمیباشد. نتایج آزمون ریشه واحد فصلی موید آن است که قیمتهای ماهانه گوشت مرغ در ایران از یک فرآیند فصلی تصادفی ناایستا پیروی نموده و مدل پایه-رگرسیونی جمعی فصلی، چارچوب مناسبی برای پیش بینی مقادیر آتی آن فراهم مینماید. یافته مطالعه بیانگر این مطالب است اگرچه مدل SARIMA کاندیدی مناسبی برای این الگوسازی است ولی نتایج معیار RMSE از خطاهای پیش بینی در افق های زمانی متفاوت حکایت از مناسب بودن مدل پایه-رگرسیونی دارد، لذا پارامترهای برآورد شده این مدل می تواند برای پیش بینی مقادیر آتی قیمت ماهانه گوشت مرغ در ایران بکار گرفته شود.
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