Economics - Research Publications
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ItemMinimum distance estimation of parametric Lorenz curves based on grouped data(Taylor & Francis, 2020-01-01)The Lorenz curve, introduced more than 100 years ago, remains as one of the main tools for analysis of inequality. International institutions such as the World Bank collect and publish grouped income data in the form of population and income shares for a large number of countries. These data are often used for estimation of parametric Lorenz curves which in turn form the basis for most inequality analyses. Despite the prevalence of parametric estimation of Lorenz curves from grouped data, and the existence of well-developed nonparametric methods, a formal description of rigorous methodology for estimating parametric Lorenz curves from grouped data is lacking. We fill this gap. Building on two data generating mechanisms, efficient methods of estimation and inference are described; several results useful for comparing the two methods of inference, and aiding computation, are derived. Simulations are used to assess the estimators, and curves are estimated for some example countries. We also show how the proposed methods improve upon World Bank methods and make recommendations for improving current practices.
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ItemUsing the GB2 Income Distribution(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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ItemEstimation and testing of stochastic frontier models using variational Bayes(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.
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ItemSome models for stochastic frontiers with endogeneity(ELSEVIER SCIENCE SA, 2016-02)
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ItemOn GMM estimation of distributions from grouped data(Elsevier, 2015)For estimating distributions from grouped data, setting up moment conditions in terms of group shares and group means leads to an optimal weight matrix and a GMM objective function that are considerably simpler than those from a previous specification. Minimization is more efficient and convergence is more reliable.