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New Agricultural Tractor Manufacturer’s Suggested Retail Price (MSRP) Model in Europe

Author

Listed:
  • Ivan Herranz-Matey

    (Departamento de Ingeniería Agroforestal, ETSIAAB, Universidad Politécnica de Madrid, Av. Puerta de Hierro 2, 28040 Madrid, Spain)

  • Luis Ruiz-Garcia

    (Departamento de Ingeniería Agroforestal, ETSIAAB, Universidad Politécnica de Madrid, Av. Puerta de Hierro 2, 28040 Madrid, Spain)

Abstract

Research investigating models for assessing new tractor pricing is notably scarce, despite its fundamental importance in conducting comprehensive cost analyses. This study aims to identify a model that is both user-friendly and robust, evaluating both parametric and Machine Learning-optimized non-parametric models. Among parametric models, the second-order polynomial model demonstrated superior performance in terms of R-squared (R 2 ) of 0.97469 and a Root Mean Square Error (RMSE) of 15,633. Conversely, Machine Learning-optimized Gaussian Processes Regressions exhibited the most favorable overall R-squared (R 2 ) of 0.99951 and a Root Mean Square Error (RMSE) of 2321. While the parametric polynomial model offers a solution with minimal mathematical and computational complexity, the non-parametric GPR model delivers highly robust outcomes, presenting stakeholders involved in new agriculture tractor transactions with superior data-driven decision-making capabilities.

Suggested Citation

  • Ivan Herranz-Matey & Luis Ruiz-Garcia, 2024. "New Agricultural Tractor Manufacturer’s Suggested Retail Price (MSRP) Model in Europe," Agriculture, MDPI, vol. 14(3), pages 1-21, February.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:3:p:342-:d:1343080
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    References listed on IDEAS

    as
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