Part of conventional wisdom glaned from econometric theory and the "learning" literature is that agents ahould use all the data they have for prediction. In this paper it is shown that agents can improve their prediction by throwing away data.

We propose a Bayesian procedure for multiple outlier detection in linear models avoiding the masking problem. Our proposal is illustrated with several examples in which our procedure outperforms other recent methods for multiple outlier detection. The posterior probalities of each data point...

In this paper we address the issue of the efficient estimation of the cointegrating vector in linear regression models with variables that follow general (higher order and fractionally) integrated processes.