Abstract
Economic forecasting may go badly awry when there are structural breaks, such that the relationships between variables that held in the past are a poor basis for making predictions about the future. We review a body of research that seeks to provide viable strategies for economic forecasting when past relationships can no longer be relied upon. We explain why model mis-specification by itself rarely causes forecast failure, but why structural breaks, especially location shifts, do. That serves to motivate possible approaches to avoiding systematic forecast failure, illustrated by forecasts for UK GDP growth and unemployment over the recent recession.
Highlights
Economic forecasting may go badly awry when there are structural breaks, such that the relationships between variables that held in the past are a poor basis for making predictions about the future
From the early days of model-based economic forecasting, the difficulties posed by breaks have been recognized: salient examples include Smith (1929) (Judging the Forecast for 1929, published in early 1929), Shoup et al (1941) (‘The times are so different [i.e., October 1941] from 1935–1939 that relations existing may not exist at all today.’—even prior to the USA entering World War II), and Klein (1947) (‘Would the econometrician merely substitute into his equations of peacetime behavior patterns in order to forecast employment in a period during which there will be a war?’)
We have argued that structural breaks are the main culprit: other putative causes of forecast failure, such as model mis-specification, turn out to be relatively benign in the absence of breaks
Summary
The ‘Great Recession’, 2008–2012 has forcefully reminded us that the Business Cycle is most certainly not dead. While forecasting remains central to the policy process in most OECD economies, forecast failure—a significant deterioration in forecast performance relative to the anticipated outcome—was a common occurrence over the ‘Great Recession’. We consider such a failure for UK GDP below, and contrast that with the ease with which unemployment could be forecasted, suggesting a change in the relationship between these key variables.
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