PRICE FORECASTING OF FULLY DRESSED CHICKEN IN THE PHILIPPINES

Leomar M. Sabroso; Joeteddy B. Bugarin

doi:10.46827/ejefr.v7i2.1459

著者

Leomar M. Sabroso MSAgE, LPT, University of Mindanao, Philippines
Joeteddy B. Bugarin PhD, University of Southeastern Philippines, Philippines

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https://doi.org/10.46827/ejefr.v7i2.1459

キーワード:

chicken、 forecasting、 ARMA/ARIMA、 chicken price

要旨

The Philippine chicken industry has been dominated by backyard farmers. This study was conducted to determine the market trend and forecast the price of fully dressed chicken in Davao City. A monthly time series of secondary data was used in the study obtained from the Philippine Statistics Authority (PSA) from January 1990 to January 2021, analyzed using an ARMA/ARIMA model to forecast the price and analyze the trend. The result revealed that the best-fit model was ARIMA (3, 1, 1), which indicated high price volatility throughout the analysis (p-value = 0.000) and the model indicates that the forecasted price from 2021-2024 is consistent.

JEL: Q10; Q12; Q13

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著者略歴

Leomar M. Sabroso、 MSAgE, LPT, University of Mindanao, Philippines

Mr. Leomar M. Sabroso is a License Professional Teacher, an Associate Professor and currently the Program Head of Business Economics at the University of Mindanao, Philippines under the College of Business Administration Education. Sabroso’s research interests focus of Agricultural Economics, AgriBusiness Economics, Production Economics and Environmental Economics.
Orcid: https://orcid.org/0000-0002-0553-3303
ResearchGate: https://www.researchgate.net/profile/Leomar-Sabroso
AcademiaEdu: https://umindanao.academia.edu/LeomarSabroso
Joeteddy B. Bugarin、 PhD, University of Southeastern Philippines, Philippines

Dr. Joeteddy B. Bugarin, Center Director, Mindanao Center for Policy Studies; PhD – Development Research Administration, University of Southeastern Philippines, Philippines. Research Interests: Economic Growth, Foreign Investments and Supply Chains.
Orcid: https://orcid.org/0000-0002-3748-3961
ResearchGate: https://www.researchgate.net/profile/Joeteddy-Bugarin
AcademiaEdu: https://usep.academia.edu/FirstNameLastName
Institutional webpage: https://www.usep.edu.ph

参考文献

Akbar, M. (2021). Analysis Factors Affecting the Demand and Supply of Free-range Chicken Meat in Pakpak Bharat Regency. Budapest International Research and Critics Institute (BIRCI-Journal): Humanities and Social Sciences. https://doi.org/10.33258/birci.v4i1.1718

Astuti, H. B., Fauzi, E., Putra, W. E., Alfayanti, A., & Ishak, A. (2021). Estimating Model Forecasting the Price of Chicken Eggs in the City of Bengkulu. AGRITEPA: Jurnal Ilmu Dan Teknologi Pertanian. https://doi.org/10.37676/agritepa.v8i2.1334

Bagnato, L., de Capitani, L., & Punzo, A. (2017). A diagram to detect serial dependencies: an application to transport time series. Quality and Quantity. https://doi.org/10.1007/s11135-016-0426-y

Bakar, N. A., & Rosbi, S. (2017). Autoregressive Integrated Moving Average (ARIMA) Model for Forecasting Cryptocurrency Exchange Rate in High Volatility Environment: A New Insight of Bitcoin Transaction. International Journal of Advanced Engineering Research and Science. https://doi.org/10.22161/ijaers.4.11.20

Bedford, R. (2020). Philippine Broiler Market Trends and Prospects. Manila: The United States Department of Agriculture (USDA), Foreign Agricultural Service.

Box, G. E., Jenkins, G. M., & Bacon, D. W. (1967). Models For Forecasting Seasonal and Non-Seasonal Time Series. Retrieved from https://www.semanticscholar.org/paper/MODELS-FOR-FORECASTING-SEASONAL-AND-NON-SEASONAL-Box-Jenkins/bffca73dad7a288d6cd1176a967e4ae3a3c9916e

Chatfield, C. (1984). The Analysis of Time Series: An Introduction. In The Analysis of Time Series: An Introduction. https://doi.org/10.1007/978-1-4899-2921-1

Chattopadhyay, A. K., & Chattopadhyay, T. (2014). Time series analysis. In Springer Series in Astrostatistics. https://doi.org/10.1007/978-1-4939-1507-1_9

Department of Agriculture (DA). (2021). DA chief highlights poultry sector’s crucial role in agri growth. Quezon City: Department of Agriculture.

Department of Agriculture. (2022). The Philippine Poultry Broiler Industry Roadmap (2022-2040). Quezon City: Department of Agriculture - Bureau of Agricultural Research.

Dürre, A., Fried, R., & Liboschik, T. (2015). Robust estimation of (partial) autocorrelation. In Wiley Interdisciplinary Reviews: Computational Statistics. https://doi.org/10.1002/wics.1351

Global Ag Media. (2009, November 30). Philippine Govt Sets Pork and Chicken Guide Prices. Retrieved from The Poultry Site: https://www.thepoultrysite.com/news/2009/11/philippine-govt-sets-pork-and-chicken-guide-prices

Hassani, H., & Yeganegi, M. R. (2019). Sum of squared ACF and the Ljung–Box statistics. Physica A: Statistical Mechanics and Its Applications. https://doi.org/10.1016/j.physa.2018.12.028

Hassani, H., & Yeganegi, M. R. (2020). Selecting optimal lag order in Ljung–Box test. Physica A: Statistical Mechanics and Its Applications. https://doi.org/10.1016/j.physa.2019.123700

Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. Melbourne: OTexts.

MacDonald, G. (2015). Elements of Time Series Econometrics: An Applied Approach, 2nd edition, by Evžen Kočenda and Alexandr Černý (Karolinum Press, Prague, 2014), pp. 220. Economic Record. https://doi.org/10.1111/1475-4932.12197

Mgaya, J. F. (2019). Application of ARIMA models in forecasting livestock products consumption in Tanzania. Cogent Food and Agriculture. https://doi.org/10.1080/23311932.2019.1607430

Moffat, I. U., & Akpan, E. A. (2019). White Noise Analysis: A Measure of Time Series Model Adequacy. Applied Mathematics. https://doi.org/10.4236/am.2019.1011069

Paduloh, P., Yuhan, N., Muhazir, A., Zulkarnaen, I., Widyantoro, M., Ilahy, A. R., & Rosihan. (2021). Design Model Forecasting and Delivery Requirement Planning for Fast Food Product. 13th-ISIEM.

Petrova, A., & Deyneka, M. (2022). ARIMA-Models: Modeling and Forecasting Prices of Stocks. International Scientific Journal “Internauka”. Series: “Economic Sciences.” https://doi.org/10.25313/2520-2294-2022-2-7921

Pezlarová, V. (2018). Analysis of the relation between the price of fodder mixture ingredients and the price of chicken meat. Agricultural Economics (Zemědělská Ekonomika). https://doi.org/10.17221/5359-agricecon

Philippine Statistics Authority (PSA). (2021). 2016-2020 Livestock and Poultry Statistics of the Philippines. Quezon City: Philippine Statistics Authority.

Ramos, K. G., & Ativo, I. J. O. (2023). Forecasting Monthly Prices of Selected Agricultural Commodities in The Philippines Using ARIMA Model. International Journal of Research Publication and Reviews. https://doi.org/10.55248/gengpi.2023.4157

Robert Nau (2019). Statistical forecasting: notes on regression and time series analysis. Fuqua School of Business Duke University.

Said, S. E., & Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika. https://doi.org/10.1093/biomet/71.3.599

Talavera, C. (2022). Chicken prices up; DA blames low production. The Philippine Star.

Vishwas, B. V., & Patel, A. (2020). Hands-on Time Series Analysis with Python. In Hands-on Time Series Analysis with Python. https://doi.org/10.1007/978-1-4842-5992-4

Wickramarachchi, A. R., Herath, H. M. L. K., Jayasinghe-Mudalige, U. K., Edirisinghe, J. C., Udugama, J. M. M., Lokuge, L. D. M. N., & Wijesuriya, W. (2017). An Analysis of Price Behavior of Major Poultry Products in Sri Lanka. Journal of Agricultural Sciences. https://doi.org/10.4038/jas.v12i2.8231

Widarjono, A., & Ruchba, S. M. (2021). Demand for Meat in Indonesia: Censored AIDS Model. Agris On-Line Papers in Economics and Informatics. https://doi.org/10.7160/aol.2021.130209

PRICE FORECASTING OF FULLY DRESSED CHICKEN IN THE PHILIPPINES

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