Measuring GDP Forecast Uncertainty Using Quantile Regressions [E-Book] / Thomas Laurent and Tomasz Koźluk
Laurent, Thomas.
Koźluk, Tomasz.
Paris : OECD Publishing, 2012
34 p. ; 21 x 29.7cm.
englisch
10.1787/5k95xd76jvvg-en
OECD Economics Department Working Papers ; 978
Economics
Full Text
Uncertainty is inherent to forecasting and assessing the uncertainty surrounding a point forecast is as important as the forecast itself. Following Cornec (2010), a method to assess the uncertainty around the indicator models used at OECD to forecast GDP growth of the six largest member countries is developed, using quantile regressions to construct a probability distribution of future GDP, as opposed to mean point forecasts. This approach allows uncertainty to be assessed conditionally on the current state of the economy and is totally model based and judgement free. The quality of the computed distributions is tested against other approaches to measuring forecast uncertainty and a set of uncertainty indicators is constructed in order to help exploiting the most helpful information.