Successful estimation of the Pareto tail index from extreme order statistics relies heavily on the procedure used to determine the number of extreme order statistics that are used for the estimation. Most of the known procedures are based on the minimization of (an estimate of) the asymptotic...

In this paper we develop a structural equation model with latent variables in an ordinal setting which allows us to test broker-dealer predictive ability of financial market movements. We use a multivariate logit model in a latent factor framework, develop a tractable estimator based on a...

In this paper, we study the finite sample accuracy of confidence intervals for index functional built via parametric bootstrap, in the case of inequality indices. To estimate the parameters of the assumed parametric data generating distribution, we propose a Generalized Method of Moment...

Statistical problems in modelling personal income distributions include estimation procedures, testing and model choice. Typically, the parameters of a given model are estimated by classical procedures such as maximum likelihood and least squares estimators. Unfortunately, the classical methods...

Choosing between two income distribution models typically involves testing two non-tested hypotheses, that is hypotheses such that one cannot be obtained as a special or limiting case of the other. Cox (1961, 1962) proposed a classical testing procedure based on the comparison of the maximised...

An important aspect of income distribution is the modelling of the data using an appropriate parametric model. This involves estimating the parameters of the models, given the data at hand. Income data are typically in grouped form. Moreover, they are not always reliable in that they may contain...

Drawing on recent work concerning the statistical robustness of inequality statistics we examine the sensitivity of poverty indices to data contamination using the concept of the influence function. We show that poverty and inequality indices have fundamentally different robustness properties,...

This paper investigates model risk issues in the context of mean-variance portfolio selection. We analytically and numerically show that, under model misspecification, the use of statistically robust estimates instead of the widely used classical sample mean and covariance is highly beneficial...

In this paper we develop a structural equation model with latent variables in an ordinal setting which allows us to test broker-dealer predictive ability of financial market movements. We use a multivariate logit model in a latent factor framework, develop a tractable estimator based on a...