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Monthly Pork Price Prediction Applying Projection Pursuit Regression: Modeling, Empirical Research, Comparison, and Sustainability Implications

Author

Listed:
  • Xiaohong Yu

    (College of Humanities and Law, Shanghai Business School, Shanghai 200235, China)

  • Bin Liu

    (Shanghai Institute of Commercial Development, Shanghai Business School, Shanghai 200235, China)

  • Yongzeng Lai

    (Department of Mathematics, Wilfrid Laurier University, Waterloo, ON N2L 3C5, Canada)

Abstract

The drastic fluctuations in pork prices directly affect the sustainable development of pig farming, agriculture, and feed processing industries, reducing people’s happiness and sense of gain. Although there have been extensive studies on pork price prediction and early warning in the literature, some problems still need further study. Based on the monthly time series data of pork prices and other 11 influencing prices (variables) such as beef, hog, piglet, etc., in China from January 2000 to November 2023, we have established a project pursuit auto-regression (PPAR) and a hybrid PPAR (H-PPAR) model. The results of the PPAR model study show that the monthly pork prices in the lagged periods one to three have an important impact on the current monthly pork price. The first lagged period has the largest and most positive impact. The second lagged period has the second and a negative impact. We built the H-PPAR model using the 11 independent variables (prices), including the prices of corn, hog, mutton, hen’s egg, and beef in lagged period one, the piglet’s price in lagged period six, and by deleting non-important variables. The results of the H-PPAR model show that the hog price in lagged period one is the most critical factor, and beef price and the other six influencing variables are essential factors. The model’s performance metrics show that the PPAR and H-PPAR models outperform approaches such as support vector regression, error backpropagation neural network, dynamic model average, etc., and possess better suitability, applicability, and reliability. Our results forecast the changing trend of the monthly pork price and provide policy insights for administrators and pig farmers to control and adjust the monthly pork price and further enhance the health and sustainable development of the hog farming industry.

Suggested Citation

  • Xiaohong Yu & Bin Liu & Yongzeng Lai, 2024. "Monthly Pork Price Prediction Applying Projection Pursuit Regression: Modeling, Empirical Research, Comparison, and Sustainability Implications," Sustainability, MDPI, vol. 16(4), pages 1-26, February.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:4:p:1466-:d:1336296
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    References listed on IDEAS

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