Model selection and model averaging are two important techniques to obtain practical and useful models in applied research. However, it is now well-known that many complex issues arise, especially in the context of model selection, when the stochastic nature of the selection process is ignored...

We discuss the impact of tuning parameter selection uncertainty in the context of shrinkage estimation and propose a methodology to account for problems arising from this issue: Transferring established concepts from model averaging to shrinkage estimation yields the concept of shrinkage...

Considerable intellectual progress has been made to the development of various semiparametric varying-coefficient models over the past ten to fifteen years. An important advantage of these models is that they avoid much of the curse of dimensionality problem as the nonparametric functions are...

Multinomial and ordered Logit models are quantitative techniques which are used in a range of disciplines nowadays. When applying these techniques, practitioners usually select a single model using either information-based criteria or pretesting. In this paper, we consider the alternative...

In this paper the Stein variance Double k-class estimator is utilized to address the omitted variable issue in hedonic price modelling. If important housing attributes are excluded from the model, the estimated implicit prices of housing attributes would drift away from the true parameter value....

In recent years, the suggestion of combining models as an alternative to selecting a single model from a frequentist prospective has been advanced in a number of studies. In this paper, we propose a new semi-parametric estimator of regression coe¢ cients, which is in the form of a feasible...