Bayesian assessment of Lorenz and stochastic dominance

David Lander, David Gunawan (david.gunawan40@gmail.com), William Griffiths (wegrif@unimelb.edu.au) and Duangkamon Chotikapanich (duangkamon.chotikapanich@monash.edu)

No 15/17, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: We introduce a Bayesian approach for assessing Lorenz and stochastic dominance. For two income distributions, say X and Y, estimated via Markov chain Monte Carlo, we describe how to compute posterior probabilities for (i) X dominates Y, (ii) Y dominates X, and (iii) neither Y nor X is dominant. The proposed approach is applied to Indonesian income distributions using mixtures of gamma densities that ensure flexible modelling. Probability curves depicting the probability of dominance at each population proportion are used to explain changes in dominance probabilities over restricted ranges relevant for poverty orderings. They also explain some seemingly contradictory outcomes from the p-values of some sampling theory tests.

Keywords: Dominance probabilities; poverty comparisons; MCMC; gamma mixture. (search for similar items in EconPapers)
JEL-codes: C11 C12 D31 I32 (search for similar items in EconPapers)
Pages: 41
Date: 2017
New Economics Papers: this item is included in nep-cta, nep-ore and nep-sea
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Bayesian assessment of Lorenz and stochastic dominance (2020)
Working Paper: Bayesian Assessment of Lorenz and Stochastic Dominance (2017)
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