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Models and muddles: comment on ‘Calibration of agricultural risk programming models using positive mathematical programming’

Petsakos Athanasios, Rozakis Stylianos

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URI: http://purl.tuc.gr/dl/dias/541AA35C-9DC4-4F8F-BB20-1C683E7FCEC9
Έτος 2022
Τύπος Παρατήρηση/Σχολιασμός
Άδεια Χρήσης
Λεπτομέρειες
Βιβλιογραφική Αναφορά A. Petsakos and S. Rozakis, “Models and muddles: comment on ‘Calibration of agricultural risk programming models using positive mathematical programming’,” Aust. J. Agric. Resour. Econ., vol. 66, no. 3, pp. 713–728, July 2022, doi: 10.1111/1467-8489.12407. https://doi.org/10.1111/1467-8489.12407
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Περίληψη

There is an emerging strand in the agricultural economics literature which examines the calibration of risk programming models using the principles of Positive Mathematical Programming (PMP). In a recent contribution to this journal, Liu et al. (2020) compare three different PMP approaches and attempt to find the ‘most practical’ method for calibrating risk programming models to be used in policy analysis. In this article, we argue that the comparison design by Liu et al. (2020) is problematic, as it is based on inappropriate metrics and it ignores recent advancements in PMP. This word of caution intends to provide constructive criticism and aims at contributing to the use of risk programming models in policy analysis.

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