摘要：Empirical distributions in nance and economics might show heavy
tails, volatility clustering, varying mean returns and multimodality as part of their
features. However, most statistical models available in the literature assume some
kind of parametric form (clearly neglecting important characteristics of the data)
or focus on modeling extreme events (therefore, providing no information about
the rest of the distribution). In this paper we develop a Bayesian nonparamet-
ric prior for a collection of distributions evolving in discrete time. The prior is
constructed by de ning the distribution at any time point as a Dirichlet process
mixture of Gaussian distributions, and inducing dependence through the atoms
of their stick-breaking decomposition. A general construction, which allows for
trends, periodicities and regressors is described. The resulting model is applied to
the estimation of the time-varying travel expense distribution of employees from
a major development bank comparable to the IDB, IMF and World Bank.
