Price trends of Agave Mezcalero in Mexico using multiple linear regression models

Angel Cruz-Ramírez, Gabino Martínez-Gutiérrez, Alberto Gabino Martinez Hernandez (), Isidro Morales and Cirenio Escamirosa-Tinoco
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Angel Cruz-Ramírez: IPN - Instituto Politecnico Nacional [Mexico]
Gabino Martínez-Gutiérrez: IPN - Instituto Politecnico Nacional [Mexico]
Alberto Gabino Martinez Hernandez: Université Sorbonne Paris Nord
Isidro Morales: IPN - Instituto Politecnico Nacional [Mexico]
Cirenio Escamirosa-Tinoco: IPN - Instituto Politecnico Nacional [Mexico]

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Abstract: This study developed a multiple linear regression model to estimate the Average rural prices (ARP) in Mexico with information taken from the period 1999-2018. The variables used to generate this model were the supply and demand as represented by planted area, yield, exports and the ARP of Agave Tequilero and Mezcalero. The analysis was carried out through the multiple linear regression model (MLRM) with the least squares method and using the statistical package R. The following variables were identified as having a significant influence on the determination of the ARP: the yield of Agave Mezcalero (YAM), the ARP of Agave Tequilero and the new planted area of Agave Tequilero (NPAATt-6) with an adjustment of 6 periods. Overall, three models were generated: model 2 was considered the most appropriate because it allows carrying out future forecasts with the new planted area with Agave Tequilero with 2 independent variables. YAM and NPAATt-6 were useful in predicting 65.5% of the annual variations in the ARP and helped recognize the negative trend of the Agave price from 2020 to 2024. Therefore, the use of the MLRM to estimate the Agave ARP can be a useful tool in predicting the performance of this crop.

Keywords: Agave Mezcalero; time series analysis; price forecast; análise de séries temporais; previsão de preços (search for similar items in EconPapers)
Date: 2023
Note: View the original document on HAL open archive server: https://hal.science/hal-04615357
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Published in Ciência Rural, 2023, 53, ⟨10.1590/0103-8478cr20210685⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04615357

DOI: 10.1590/0103-8478cr20210685

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