The paper considers the problem of statistical inference with estimated Lorenz curves and income shares. The full variance-covariance structure of the (asymptotic) normal distribution of a vector of Lorenz curve ordinates is derived and shown to depend only on conditional first and second moments that can be estimated consistently without prior specification of the population density underlying the sample data. Lorenz curves and income shares can thus be used as tools for statistical inference instead of simply as descriptive statistics.

This paper discusses several statistical techniques which can be used to test the validity of a possibly nonlinear and multivariate regression model, using the information provided by estimating one or more alternative models on the same set of data. The techniques we propose can be regarded as alternative implementations of Cox's idea for non-nested hypothesis testing; under the null hypothesis, all of the test statistics are asymptotically the same random variable. For the univariate linear regression case, our test and Pesaran's has asymptotic relative efficiency of unity for local alternatives. Finally, we present sampling experiments for univariate linear models which show that the small-sample performance of our J test and Pesaran's test can be quite different.

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Several procedures are proposed for testing the specification of an econometric model when one or more models purport to explain the same phenomenon. These procedures are closely related, although not identical, to non-nested hypothesis tests proposed by Pesaran and Deaton, and have similar asymptotic properties. They are simple conceptually and computationally, and unlike earlier techniques, may be used to test against several alternative models simultaneously. Some empirical results suggest that ability of the tests to reject false hypotheses is likely to be good in practice.