Wolfgang Britz
Published: 14.07.2021 〉 Volume 70 (2021), Number 3, 165-181 〉 Resort: Articles
Submitted: 22.01.2021 〉 Feedback to authors after first review: 16.04.2021 〉 Accepted: 20.04.2021
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.