This paper studies the local robustness of estimators and tests for the conditional location and scale parameters in a strictly stationary time series model. We first derive optimal bounded-influence estimators for such settings under a conditionally Gaussian reference model. Based on these...

In the framework of generalized linear models, the nonrobustness of classical estimators and tests for the parameters is a well known problem and alternative methods have been proposed in the literature. These methods are robust and can cope with deviations from the assumed distribution....

Indirect inference (Smith, 1993; Gouriéroux, Monfort and Renault, 1993) is a simulation-based estimation method dealing with econometric models whose likelihood function is intractable. Typical examples are diffusion models described by stochastic differential equations. A potential problem that...

With income distributions it is common to encounter the problem of missing data. When a parametric model is fitted to the data, the problem can be overcome by specifying the marginal distribution of the observed data. With classical methods of estimation such as the maximum likelihood (ML) an...

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...

Lorenz curves and second-order dominance criteria are known to be sensitive to data contamination in the right tail of the distribution. We propose two ways of dealing with the problem: (1) Estimate Lorenz curves using parametric models for income distributions, and (2) Combine empirical...

With income distributions it is common to encounter the problem of missing data. When a parametric model is fitted to the data, the problem can be overcome by specifying the marginal distribution of the observed data. With classical methods of estimation such as the maximum likelihood (ML) an...