Model choice and size distribution: a Bayequentist approach

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We propose a new three-step model-selection framework for size distributions in empirical data. It generalizes a recent frequentist plausibility-of-fit analysis (Step 1) and combines it with a relative ranking based on the Bayesian Akaike Information Criterion (Step 2). We enhance these statistical criteria with the additional criterion of microfoundation (Step 3) which is to select the size distribution that comes with a dynamic micro model of size dynamics. A numerical performance test of Step 1 shows that our generalization is able to correctly rule out the distribution hypotheses unjustified by the data at hand. We then illustrate our approach, and demonstrate its usefulness, with a sample of commercial cattle farms in Namibia. In conclusion, the framework proposed here has the potential to reconcile the ongoing debate about size distribution models in empirical data, the two most prominent of which are the Pareto and the lognormal distribution.
Original languageEnglish
JournalAmerican Journal of Agricultural Economics
Volume97
Issue number3
Pages (from-to)978-997
Number of pages20
ISSN0002-9092
DOIs
Publication statusPublished - 04.2015

    Research areas

  • Sustainability sciences, Management & Economics - cattle farming, environmental risk, Gibrat's Law, hypothesis testing, model choice, model selection, Pareto distribution, rank-size rule, semiarid rangelands, size distributions

DOI

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