Robustifying forecasts from equilibrium-correction models

Cointegration analysis has led to equilibrium-correction econometric systems being ubiquitous. But in a non-stationary world subject to structural breaks, where model and mechanism differ, equilibrium-correction models are a risky device from which to forecast. Equilibrium shifts entail systematic forecast failure, as forecasts will tend to move in the opposite direction to data. We explain the empirical success of second-differenced devices and of model transformations based on additional differencing as reducing forecast-error biases, at some cost in increased forecast-error variances. The analysis is illustrated by an empirical application to narrow money holdings in the UK.