Bayesian dynamic linear models (DLM) are useful in time series modelling because of the flexibility that they present in obtaining a good forecast. They are based on a decomposition of the relevant factors which explain the behavior of the series through a series of state parameters....

In this article we examine how model selection in neural networks can be guided by statistical procedures such as hypotheses tests, information criteria and cross validation. The application of these methods in neural network models is discussed, paying attention especially to the identification...

The economic analysis of corporate governance is en vogue. In addition to a host of theoretical papers, an increasing number of empirical studies analyze how ownership structure, capital structure, the structure of the board and the market for corporate control influence firm performance. This...