Cross-validation is a method used to estimate the expected prediction error of a model. Such estimates may be of interest in themselves, but their use for model selection is more common. Unfortunately, cross-validation is viewed as being computationally expensive in many situations. In this...

A new class of parametric regression models for both under- and over-dispersed count data is proposed. These models are based on squared polynomial expansions around a Poisson baseline density. The approach is similar to that for continuous data using squared Hermite polynomials proposed by...

This re-analysis of Tobin's (l950)study makes three points: (1) graphs are a powerful device for discovery and for communication, and can reveal much of the information in the data; (2) squeezing out the more subtle multivariate messages requires some solution to the usual overparameterization...

Using the Kalman filter, we obtain maximum likelihood estimates of a permanent-transitory components model for log spot and forward dollar prices of the pound, the franc, and the yen. This simple parametric model is useful in understanding why the forward rate may be an unbiased predictor of the...