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Rival theoretical explanations of events and phenomena are common in economics, of which the Keynesian versus the monetarist debate is perhaps the best known. The traditional empirical procedure in economics has been to formulate a model from each theoretical framework and test each model's restrictions against observed data, rejecting or corroborating the specific model accordingly as the evidence is adverse or favourable. Although such an approach seems objective and scientific, it has two serious drawbacks: in practice neither rejection nor corroboration are definitive.

To reconcile forecast failure with building congruent empirical models, we analyze the sources of mis‐prediction. This reveals that ex ante forecast failure is purely a function of forecast‐period events, not determinable from in‐sample information. The primary causes are unmodelled shifts in deterministic factors, rather than model mis‐specification, collinearity, or a lack of parsimony. We examine the effects of deterministic breaks on equilibrium‐correction mechanisms, and consider the role of causal variables. Throughout, Monte Carlo simulation and empirical models illustrate the analysis, and support a progressive research strategy based on learning from past failures.

The properties of automatic model selection are discussed, focusing on PcGets. We explain the background concepts & why automatic methods can perform well. Criticisms of model selection procedures are noted & rebutted. The algorithm is sketched, distinguishing between costs of search & costs of inference: the latter are unavoidable in any statistical science, whereas the costs of searching seem small in comparison. The choice of a 'search strategy' & the actual simulation performance of the approach are discussed. We outline a number of developments that will improve the behavior, & generalize the scope, of such programs, & tackle hitherto intractable problems. 1 Figure, 58 References. [Copyright 2004 Elsevier Ltd.]