Automated Calibration of Farm-Sale Mixed Linear Programming Models using Bi-Level Programming

Wolfgang Britz

German Journal of Agricultural Economics, 2021, vol. 70, issue 03

Abstract: We calibrate Linear and Mixed Integer Programs with a bi-level estimator, minimizing under First-order-con-ditions (FOC) conditions a penalty function consider-ing the calibration fit and deviations from given param-eters. To deal with non-convexity, a heuristic generates restart points from current best-fit parameters and their means. Monte-Carlo analysis assesses the ap-proach 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 cal-ibration targets. The approach recovers the true pa-rameters and thus allows for systematic and automated calibration.

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Date: 2021
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Working Paper: Automated calibration of farm-scale mixed linear programming models using bi-level programming (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:ags:gjagec:343299

DOI: 10.22004/ag.econ.343299

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