Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/305125 
Authors: 
Year of Publication: 
2021
Citation: 
[Journal:] German Journal of Agricultural Economics (GJAE) [ISSN:] 2191-4028 [Volume:] 70 [Issue:] 3 [Year:] 2021 [Pages:] 165-181
Publisher: 
Deutscher Fachverlag, Frankfurt a. M.
Abstract: 
We calibrate Linear and Mixed Integer Programs with a bi-level estimator, minimizing under First-order-conditions (FOC) conditions a penalty function considering the calibration fit and deviations from given parameters. To deal with non-convexity, a heuristic generates restart points from current best-fit parameters and their means. Monte-Carlo analysis assesses the approach by drawing parameters for a model optimizing acreages under maximal crop shares, a land balance and annual plus intra-annual labour constraints; a variant comprises integer based investments. Resulting optimal solutions perturbed by white noise provide calibration targets. The approach recovers the true parameters and thus allows for systematic and automated calibration.
Subjects: 
linear programming
mixed linear programming
calibration
bi-level programming
farm-scale model
Persistent Identifier of the first edition: 
Document Type: 
Article

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