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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.

This book provides a formal analysis of the models, procedures, and measures of economic forecasting with a view to improving forecasting practice. David Hendry and Michael Clements base the analyses on assumptions pertinent to the economies to be forecast, viz. a non-constant, evolving economic system, and econometric models whose form and structure are unknown a priori. The authors find that conclusions which can be established formally for constant-parameter stationary processes and correctly-specified models often do not hold when unrealistic assumptions are relaxed. Despite the difficulty of proceeding formally when models are mis-specified in unknown ways for non-stationary processes that are subject to structural breaks, Hendry and Clements show that significant insights can be gleaned. For example, a formal taxonomy of forecasting errors can be developed, the role of causal information clarified, intercept corrections re-established as a method for achieving robustness against forms of structural change, and measures of forecast accuracy re-interpreted