The business cycle and economic forecasting

Macroeconomic forecasts are traditionally stated as point estimates. Retrospective evaluations of forecasts usually assume that the cost of a forecast error increases with the arithmetic magnitude of the error. As a result, measures such as the root-mean-square error (RMSE) or the mean absolute error (MAE) are most often used to summarize forecast performance.

For many users, however, the traditional approach may not correspond with their own uses and evaluations of macro forecasts. The premise of this chapter is that it could be valuable for many users to accurately predict the stage of the business cycle several quarters ahead. For example, a government policymaker accountable to the electorate might well want the economy to be expanding in the quarter before an election; the actual levels of real GNP and other variables would be of secondary importance. Another example is that a producer of capital goods might expect two quite different sales levels to be associated with a particular level of real GNP, depending on whether the economy is expanding or contracting.

In short, when a variable such as real GNP is predicted, the relevant loss function may not be a simple linear or quadratic function of the forecast error. This chapter therefore proposes a measure to supplement the traditional summaries of forecast errors. The new measure attempts to capture the extent to which the stage of the business cycle is accurately predicted.