This paper proposes a tripartite framework of design, evaluation, and post-evaluation analysis for generating and interpreting economic forecasts. This framework?s value is illustrated by re-examining mean square forecast errors from dynamic models and nonlinearity biases from empirical...

Hoover and Perez (1999) advocate a constructive approach to data mining. The current paper identifies four pejorative senses of data mining and shows how Hoover and Perez?s approach counters each. To assess the benefits of constructive data mining, the current paper applies a data-mining...

This paper provides densities and finite sample critical values for the single-equation error correction statistic for testing cointegration. Graphs and response surfaces summarize extensive Monte Carlo simulations and highlight simple dependencies of the statistic's quantiles on the number of...

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...

Kevin Hoover and Stephen Perez take important steps towards resolving some key issues in econometric methodology. They simulate general-to-specific selection for linear, dynamic regression models, and find that their algorithm performs well in re-mining the ?Lovell database?. We discuss...