Measuring GDP Forecast Uncertainty Using Quantile Regressions:

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...

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Bibliographische Detailangaben
1. Verfasser: Laurent, Thomas (VerfasserIn)
Weitere Verfasser: Koźluk, Tomasz (MitwirkendeR)
Format: Elektronisch E-Book
Sprache:English
Veröffentlicht: Paris OECD Publishing 2012
Schriftenreihe:OECD Economics Department Working Papers
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Zusammenfassung: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
Beschreibung:1 Online-Ressource (33 Seiten) 21 x 29.7cm
DOI:10.1787/5k95xd76jvvg-en

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