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...

A structural break is viewed as a permanent change in the parameter vector of a model. Using taxonomies of all sources of forecast errors for both conditional mean and conditional variance processes, we consider the impacts of breaks and their relevance in forecasting models: (a) where the...

Economies evolve and are subject to sudden shifts precipitated by legislative changes, economic policy, major discoveries, and political turmoil. Macroeconometric models are a very imperfect tool for forecasting this highly complicated and changing process. Ignoring these factors leads to a wide...

We investigate alternative robust approaches to forecasting, using a new class of robust devices, contrasted with equilibrium-correction models. Their forecasting properties are derived facing a range of likely empirical problems at the forecast origin, including measurement errors, impulses,...

While there has been a great deal of interest in the modelling of non-linearities in economic time series, there is no clear consensus regarding the forecasting abilities of non-linear time-series models. We evaluate the performance of two leading non-linear models in forecasting post-war US...

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 analyse by simulation the impact of model-selection strategies (sometimes called pre-testing) on forecast performance in both constant-and non-constant-parameter processes. Restricted, unrestricted and selected models are compared when either of the first two might generate the data. We find...

Although out-of-sample forecast performance is often deemed to be the 'gold standard' of evaluation, it is not in fact a good yardstick for evaluating models in general. The arguments are illustrated with reference to a recent paper by Carruth, Hooker and Oswald ["Review of Economics and...