Finite mixture distributions are a weighted average of a ¯nite number of distributions. The latter are usually called the mixture components. The weights are usually described by a multinomial distribution and are sometimes called mixing proportions. The mixture components may be the same type of distributions with di®erent parameter values but they may also be completely di®erent distributions (Everitt and Hand, 1981; Titterington et al., 1985). Therefore, ¯nite mixture distributions are very °exible for modeling data. They are frequently used as a building block within many modern econometric models. The speci¯cation of the mixture distribution depends on the modeling problem at hand. In this thesis, we introduce new applications of ¯nite mixtures to deal with several di®erent modeling issues. Each chapter of the thesis focusses on a speci¯c modeling issue. The parameters of some of the resulting models can be estimated using standard techniques but for some of the chapters we need to develop new estimation and inference methods. To illustrate how the methods can be applied, we analyze at least one empirical data set for each approach. These data sets cover a wide range of research ¯elds, such as macroeconomics, marketing, and political science. We show the usefulness of the methods and, in some cases, the improvement over previous methods in the literature.

Ph.H.B.F. Franses (Philip Hans) , R. Paap (Richard)
Erasmus University Rotterdam , Tinbergen Institute
Tinbergen Instituut Research Series
Erasmus School of Economics

van Dijk, B. (2009, July 2). Essays on Finite Mixture Models (No. 458). Tinbergen Instituut Research Series. Retrieved from