Automated calibration of farm-scale mixed linear programming models using bi-level programming

Wolfgang Britz

No 303683, Discussion Papers from University of Bonn, Institute for Food and Resource Economics

Abstract: We calibrate Linear and Mixed Integer Programs with a bi-level estimator, minimizing under First-order-conditions (FOC) conditions under 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.

Keywords: Research; Methods/; Statistical; Methods (search for similar items in EconPapers)
Pages: 28
Date: 2020-05-26
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Automated Calibration of Farm-Sale Mixed Linear Programming Models using Bi-Level Programming (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:ags:ubfred:303683

DOI: 10.22004/ag.econ.303683

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