- ISSN :
- 1815-1973 (online)
- DOI :
Working papers from the Economics Department of the OECD that cover the full range of the Department’s work including the economic situation, policy analysis and projections; fiscal policy, public expenditure and taxation; and structural issues including ageing, growth and productivity, migration, environment, human capital, housing, trade and investment, labour markets, regulatory reform, competition, health, and other issues.
The views expressed in these papers are those of the author(s) and do not necessarily reflect those of the OECD or of the governments of its member countries.
Measuring GDP Forecast Uncertainty Using Quantile RegressionsClick to Access:
- Thomas Laurent1, 2, Tomasz Kozluk2
- Author Affiliations
- 1: INSEE, France
- 2: OECD, France
- Publication Date
- 06 July 2012
- Bibliographic information
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.
- forecasting, uncertainty, GDP, quantile regression
- JEL Classification:
- C31: Mathematical and Quantitative Methods / Multiple or Simultaneous Equation Models; Multiple Variables / Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
- C53: Mathematical and Quantitative Methods / Econometric Modeling / Forecasting and Prediction Methods; Simulation Methods