Output gap estimates calculated in real-time are known to be often unreliable. Recent work has found that, without the benefit of hindsight, it can prove difficult for policy-makers to pin down accurately the current position of the output gap. However, attention primarily has focussed on output gap point estimates alone. This paper considers output gap estimates and their uncertainty more generally. As is well known from the forecasting literature, if the outturn falls within the bounds of what was expected the fact that point forecasts are inaccurate need not mean forecasts more generally contain no useful information. In this sense, the unreliability of real-time output gap estimates need be neither surprising nor indeed lead to sub-optimal inference by users of the estimates. Interpreting real-time output gap estimates as forecasts, we explain the importance of providing measures of uncertainty, via interval or density forecasts, around real-time output gap estimates. We consider how this can be achieved. The importance of allowing, in particular, for parameter uncertainty is discussed. We then explain how ex post the accuracy of these measures of unreliability associated with real-time estimates can be evaluated statistically and a decision then made about their reliability. An application to the Eurozone illustrates the use of these techniques in the context of real-time output gap measurement. Simulated out-of-sample experiments reveal that not only can real-time point estimates of the Eurozone output gap be unreliable, but so can measures of uncertainty associated with them. This provides a serious challenge to both producers and users of output gap estimates.
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Paper provided by National Institute of Economic and Social Research in its series NIESR Discussion Papers with number
225.