Model choice and size distribution: a bayequentist approach

john-oliver engler and stefan baumgartner;

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 micromodel 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 log-normal distribution.