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

Ivan Herranz-Matey and Luis Ruiz-Garcia ()
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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

Agriculture, 2024, vol. 14, issue 3, 1-21

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.

Keywords: agriculture machinery; new price; non-parametric regression; machine learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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