We take a model selection approach to the question of whether a class of adaptive prediction models (artificial neural networks) is useful for predicting future values of nine macroeconomic variables. We use a variety of out-of-sample forecast-based model selection criteria, including forecast...

Let H be an infinite-dimensional real separable Hilbert space. Given an unknown mapping M:H (r)H that can only be observed with noise, we consider two modified Robbins-Monro procedures to estimate the zero point ?o ( H of M. These procedures work in appropriate finite dimensional sub-spaces of...

Abstract We provide an altrnative proof that the Ordinary Least Squares estimator is the (conditionally) best linear unbiased estimator.

This paper estimates models of electricity and gas consumption for individual households using the Miracle 4 to 6 data sets collected by San Diego Gas and Electricity Company. Two types of model were constructed: the first involves typical end-use models with consumption explained by appliance...

We construct two classes of smoothed empirical likelihood ratio tests for the conditional independence hypothesis by writing the null hypothesis as an infinite collection of conditional moment restrictions indexed by a nuisance parameter. One class is based on the CDF; another is based on smoother...