Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts
AbstractWe conduct a systematic comparison of the short-term forecasting abilities of eleven statistical models and professional analysts in a pseudo-real time setting, using a large set of monthly indicators. Our analysis covers the euro area and its five largest countries over the years 1996-2011. We find that summarizing the available monthly information in a few factors is a more promising forecasting strategy than averaging a large number of indicator-based forecasts. The dynamic and static factor model outperform other models, especially during the crisis period. Judgmental forecasts by professional analysts often embody valuable information that could be used to enhance forecasts derived from purely mechanical procedures.
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Bibliographic InfoPaper provided by Netherlands Central Bank, Research Department in its series DNB Working Papers with number 365.
Date of creation: Dec 2012
Date of revision:
nowcasting; professional forecasters; factor model; judgment; forecasting;
Find related papers by JEL classification:
- E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-01-19 (All new papers)
- NEP-ECM-2013-01-19 (Econometrics)
- NEP-EEC-2013-01-19 (European Economics)
- NEP-FOR-2013-01-19 (Forecasting)
- NEP-MAC-2013-01-19 (Macroeconomics)
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- Enrico D'Elia, 2014. "Predictions vs. preliminary sample estimates: the case of eurozone quarterly GDP," Working Papers 2, Department of the Treasury, Ministry of the Economy and of Finance.
- Cláudia Duarte, 2014. "Autoregressive augmentation of MIDAS regressions," Working Papers w201401, Banco de Portugal, Economics and Research Department.
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