Parametric and nonparametric income distribution estimators in CGE micro-simulation modeling

We consider the issue of income distribution modeling in the context of poverty analysis impact based on computable general equilibrium micro-simulation models. The empirical distribution function (EDF) is by far the most commonly used estimator in practice. It is, however, not the only available consistent estimator and there may be situations in which a different estimator would be able to provide more accurate results. An alternative is to use a smooth estimator of the population income distribution. Two types of such estimators are available: parametric and nonparametric ones. In the first case, one has to chose a particular parametric form for the distribution function and estimates its parameters. The main drawback is the difficulty associated with the selection of the functional form. The nonparametric approach sidesteps this functional form issue by using kernel density estimators that only impose mild restrictions on the distribution function. This is obviously an important advantage, but its cost is that the accuracy of these estimators typically depends to a large extent on the bandwidth used in the kernel function. Another advantage is that it nests the EDF as a special case. We propose to extend the work of Boccanfuso et al. (2008) in two ways. First, we consider a larger set of parametric functions, including the 5 parameter generalized beta distribution and some of its special cases. Second, we use non-parametric kernel estimators and study their accuracy under different bandwidth selection schemes. Lastly, we provide Monte Carlo comparisons of the accuracy of these methods with the widely used EDF. •We consider income distribution modeling for poverty analysis in CGE-micro models.•Parametric and nonparametric estimators are compared.•Real data and Monte Carlo simulations show the robustness of kernel estimators.•Kernel estimators have MSE similar to that of 4 or 5 parameter distributions.