We consider forecasting using a combination, when no model coincides with a non-constant data generation process (DGP). Practical experience suggests that combining forecasts adds value, and can even dominate the best individual device. We show why this can occur when forecasting models are...

We examine the properties of automatic model selection, as embodied in PcGets, and evaluate its performance across different (unknown) states of nature. After describing the basic algorithm and some recent changes, we discuss the consistency of its selection procedures, then examine the extent...

Parallel computation has a long history in econometric computing, but is not at all wide spread. We believe that a major impediment is the labour cost of coding for parallel architectures. Moreover, programs for specific hardware often become obsolete quite quickly. Our approach is to take a...

This paper reviews the need for powerful computing facilities in econometrics, focusing on concrete problems which arise in financial economics and in macroeconomics. We argue that the profession is being held back by the lack of easy to use generic software which is able to exploit the...

This paper describes some recent advances and contributions to our understanding of economic forecasting. The framework we develop helps explain the findings of forecasting competitions and the prevalence of forecast failure. It constitutes a general theoretical background against which recent...

Disputes about econometric methodology partly reflect a lack of evidence on alternative approaches. We reconsider econometric model selection from a computer-automation perspective, focusing on general-to-specific reductions, embodied in PcGets. Starting from a general congruent model, standard...

Since forecast failure is due to unanticipated large shifts in deterministic factors, sensible agents should adopt robust forecasting rules. Unless the model coincides with the generating mechanism, one cannot even prove that causal variables will dominate non-causal in forecasting. In such a...

Identification is an essential attribute of any models parameters, so we consider its three aspects of uniqueness, correspondence to reality and interpretability. Observationally-equivalent over-identified models can co-exist, and are mutually encompassing in the population; correctly-identified...

Although difference-stationary (DS) and trend-stationary (TS) processes have been subject to considerable analysis, there are no direct comparisons for each being the data-generation process (DGP). We examine incorrect choice between these models for forecasting for both known and estimated...

We consider information from many sciences bearing on the causes and consequences of climate change, focusing on lessons from past mass extinctions of life on Earth.  The increasing atmospheric levels of the main greenhouse gases are now established, as is their source in human activity. ...