We consider partial identification of finite mixture models in the presence of an observable source of variation in the mixture weights that leaves component distributions unchanged, as is the case in large classes of econometric models. We first show that when the number J of component...

We consider partial identification of finite mixture models in the presence of an observable source of variation in the mixture weights that leaves component distributions unchanged, as is the case in large classes of econometric models. We first show that when the number J of component...

We propose a measure of the degree of ambiguity associated with a belief function and a nonparametric method to estimate it. The degree of ambiguity associated with a belief function is measured by the Kullback-Leibler diameter of the set of probability measures compatible with it. It is shown...

We provide a test for the specification of a structural model without identifying assumptions. We show the equivalence of several natural formulations of correct specification, which we take as our null hypothesis. From a natural empirical version of the latter, we derive a Kolmogorov-Smirnov...

Time series parametric models generally cater to a particular objective, such as forecasting, and it is therefore desirable to judge such models solely on the basis of their performance in the fullfillment of that objective. We propose a specification testing procedure which concentrates power...

The long range dependence paradigm appears to be a suitable description of the data generating process for many observed economic time series. This is mainly due to the fact that it naturally characterizes time series displaying a high degree of persistence, in the form of a long lasting effect...