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  • 标题:Measuring GDP Forecast Uncertainty Using Quantile Regressions
  • 本地全文:下载
  • 作者:Thomas Laurent ; Tomasz Kozluk
  • 期刊名称:Economics Department Working Papers / OECD
  • 印刷版ISSN:0259-4633
  • 出版年度:2012
  • 卷号:2012
  • DOI:10.1787/5k95xd76jvvg-en
  • 出版社:Organisation for Economic Co-operation and Development (OECD)
  • 摘要: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.
  • 关键词:forecasting; uncertainty; GDP; quantile regression
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