This chapter summarizes recent literature on asymptotic inference about forecasts. Both analytical and simulation based methods are discussed. The emphasis is on techniques applicable when the number of competing models is small. Techniques applicable when a large number of models is compared to...

This paper provides a comprehensive framework for comparing predictors of univariate time series in the mean square norm. Initially, the forecast errors are assumed to be unbiased, independent, and normally distributed. Each of these is progressively relaxed. A new heteroscedasticity and...

This paper is concerned with the use of regression methods to predict values of a response variable when that variable is naturally ordered. An application to the prediction of student examination performance is provided and it is argued that, although individual scores are unlikely to be well...

The problematic point of the noise trader model is the difficulty of measuring noise and testing theories based on noise trading. We investigate the relationship between the trading volume and the stock returns, using the Granger-causality type of methodology, as a means of testing the noise...

This paper provides evidence on problems associated with using standard tests for predictive failure when the errors of a linear regression model are not normally distributed. The ability of a simple bootstrap procedure to give a useful degree of control over the significance levels is examined.