Linear correlation is only an adequate means of describing the dependence between two random variables when they are jointly elliptically distributed. When the joint distribution of two or more variables is not elliptical the linear correlation coefficient becomes just one of many possible ways of summarising the dependence structure between the variables. In this paper we make use of a theorem due to Sklar (1959), which shows that an n-dimensional distribution function may be decomposed into its n marginal distributions, and a copula, which completely describes the dependence between the n variables. We verify that Sklar's theorem may be extended to conditional distributions, and apply it to the modelling of the time-varying joint distribution of the Deutsche mark - U.S. dollar and Yen - U.S. dollar exchange rate returns. We find evidence that the conditional dependence between these exchange rates is time-varying, and that it is asymmetric: dependence is greater during appreciations of the U.S. dollar against the mark and the yen than during depreciations of the U.S. dollar. We also find strong evidence of a structural break in the conditional copula following the introduction of the euro.

The theory of conditional copulas provides a means of constructing flexible multivariate density models, allowing for time varying conditional densities of each individual variable, and for time-varying conditional dependence between the variables. Further, the use of copulas in constructing these models often allows for the partitioning of the parameter vector into elements relating only to a marginal distribution, and elements relating to the copula. This paper presents a two-stage (or multi-stage) maximum likelihood estimator for the case that such a partition is possible. We extend the existing statistics literature on the estimation of copula models to consider data that exhibit temporal dependence and heterogeneity. The estimator is flexible enough that the case that unequal amounts of data are available on each variable is easily handled. We investigate the small sample properties of the estimator in a Monte Carlo study, and find that it performs well in comparisons with the standard (one-stage) maximum likelihood estimator. Finally, we present an application of the estimator to a model of the joint distribution of daily Japanese yen - U.S. dollar and euro - U.S. dollar exchange rates. We find some evidence that a copula that captures asymmetric dependence performs better than those that assume symmetric dependence.

"Pay equity" or "comparable worth" are terms that have come to stand for the notion that people should be paid equally for jobs of similar skill levels, training requirements, and responsibility, regardless of their race, sex, creed, or color. Comparable worth has been called the job issue of the eighties. The first half of the decade witnessed a surge of activity around the issue in the courts, in legislative arenas, and at the bargaining table.

Although two of the original lawsuits establishing the grounds for comparable worth involved private-sector employers,1 most activity to date has dealt with public-sector employees. Recently, however, the notion of pay equity has gained renewed attention in the private sector as well. As economic shifts diminish the number of well-paid manufacturing jobs, lower-paid jobs in ser­ vices and related sectors, traditionally employing high concentra­ tions of women, become relatively more important to households and communities. The pressure is growing for private as well as public employers to redress discriminatory wage scales that have evolved in the workplace.