Integrating risk and uncertainty in PMP models
No hay miniatura disponible
Fecha
Autores
Título de la revista
ISSN de la revista
Título del volumen
Editor
Resumen
Descripción
Positive Mathematical Programming (PMP) is one of the most commonly used methods
of calibrating activity linear programming (LP) models in agriculture. PMP applications
published thus far focus on the estimation of a farm’s nonlinear cost or profit function
and rely on the recovery of unobserved or implicit information that can explain the initial
model’s inability to calibrate. In this paper we use the PMP procedure to calibrate
an expected utility model under the assumption that this implicit information can reveal a
farmer’s profit expectations and risk attitude. The perfect calibration shows that PMP can
be applied not only to LP models, but also to models that incorporate risk and this provides
an interesting alternative to the traditional PMP methodology.