Forecasting economic and financial time series with non-linear models

In this paper we discuss the current state-of-the-art in estimating, evaluating, and selecting among non-linear forecasting models for economic and financial time series. We review theoretical and empirical issues, including predictive density, interval and point evaluation and model selection, loss functions, data-mining, and aggregation. In addition, we argue that although the evidence in favor of constructing forecasts using non-linear models is rather sparse, there is reason to be optimistic. However, much remains to be done. Finally, we outline a variety of topics for future research, and discuss a number of areas which have received considerable attention in the recent literature, but where many questions remain.

Bilayer graphene formed by passage of current through graphite: evidence for a three-dimensional structure

The passage of an electric current through graphite or few-layer graphene can result in a striking structural transformation, but there is disagreement about the precise nature of this process. Some workers have interpreted the phenomenon in terms of the sublimation and edge reconstruction of essentially flat graphitic structures. An alternative explanation is that the transformation actually involves a change from a flat to a three-dimensional structure. Here we describe detailed studies of carbon produced by the passage of a current through graphite which provide strong evidence that the transformed carbon is indeed three-dimensional. The evidence comes primarily from images obtained in the scanning transmission electron microscope using the technique of high-angle annular dark-field imaging, and from a detailed analysis of electron energy loss spectra. We discuss the possible mechanism of the transformation, and consider potential applications of “three-dimensional bilayer graphene”.