Economics - Research Publications

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    Bayesian inference for health inequality and welfare using qualitative data
    Gunawan, D ; Griffiths, WE ; Chotikapanich, D (ELSEVIER SCIENCE SA, 2018-01-01)
    We show how to use Bayesian inference to compare two ordinal categorical distributions commonly occurring with data on self-reported health status. Procedures for computing probabilities for first and second order stochastic dominance and S-dominance are described, along with methodology for obtaining posterior densities for health inequality indexes. The techniques are applied to four years of data on Australian self-reported health status.
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    Using the GB2 Income Distribution
    Chotikapanich, D ; Griffiths, WE ; Hajargasht, G ; Karunarathne, W ; Rao, DSP (MDPI AG, 2018-06-01)
    To use the generalized beta distribution of the second kind (GB2) for the analysis of income and other positively skewed distributions, knowledge of estimation methods and the ability to compute quantities of interest from the estimated parameters are required. We review estimation methodology that has appeared in the literature, and summarize expressions for inequality, poverty, and pro-poor growth that can be used to compute these measures from GB2 parameter estimates. An application to data from China and Indonesia is provided.
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    Estimation and testing of stochastic frontier models using variational Bayes
    Hajargasht, G ; Griffiths, WE (Springer Verlag, 2018-10)
    We show how a wide range of stochastic frontier models can be estimated relatively easily using variational Bayes. We derive approximate posterior distributions and point estimates for parameters and inefficiency effects for (a) time invariant models with several alternative inefficiency distributions, (b) models with time varying effects, (c) models incorporating environmental effects, and (d) models with more flexible forms for the regression function and error terms. Despite the abundance of stochastic frontier models, there have been few attempts to test the various models against each other, probably due to the difficulty of performing such tests. One advantage of the variational Bayes approximation is that it facilitates the computation of marginal likelihoods that can be used to compare models. We apply this idea to test stochastic frontier models with different inefficiency distributions. Estimation and testing is illustrated using three examples.