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Flow on conjunctural information and forecast of euro area economic activity

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Author Info
Katja Drechsel () (University of Osnabrück, International Economic Policy, Rolandstrasse 8, D-49069 Osnabrück, Germany.)
Laurent Maurin () (Corresponding author: European Central Bank, Kaiserstrasse 29, D-60311 Frankfurt am Main, Germany.)

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Abstract

Euro area GDP and components are nowcast and forecast one quarter ahead. Based on a dataset of 163 series comprising the relevant monthly indicators, simple bridge equations with one explanatory variable are estimated for each. The individual forecasts generated by each equation are then pooled, using six weighting schemes including Bayesian ones. To take into consideration the release calendar of each indicator, six forecasts are compiled independently during the quarter, each based on different information sets - different indicators, different individual equations and finally different weights to aggregate information. The information content of the various blocks of information at different points in time for each GDP component is then discussed. It appears that taking into account the information flow results in significant changes in the weight allocated to each block of information, especially when the first month of hard data becomes available. This conclusion, reached for all the components and most of the weighting scheme, supports and extends the findings of Giannone, Reichlin and Small (2006) and Banbura and Ruenstler (2007). An out-of-sample forecast comparison exercise is also carried out for each component and GDP directly. The forecast performance is found to vary widely across components. Two weighting schemes are found to outperform the equal weighting scheme in almost all cases. One-quarter ahead, the direct forecast of GDP is found to outperform the bottom-up approach. However, the nowcast resulting in the lowest forecast errors is derived from the bottom-up approach. JEL Classification: C22, C53, E17.

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Paper provided by European Central Bank in its series Working Paper Series with number 925.

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Length: 53 pages
Date of creation: Aug 2008
Date of revision:
Handle: RePEc:ecb:ecbwps:20080925

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Related research
Keywords: Large dataset; forecast pooling; weighting scheme; GDP components; out-ofsample forecast performance; bottom-up vs. direct forecast.;

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References listed on IDEAS
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  1. Kapetanios, George & Labhard, Vincent & Price, Simon, 2008. "Forecasting Using Bayesian and Information-Theoretic Model Averaging: An Application to U.K. Inflation," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 33-41, January. [Downloadable!] (restricted)
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  2. Gary Koop & Simon Potter, 2003. "Forecasting in Large Macroeconomic Panels using Bayesian Model Averaging," Discussion Papers in Economics 04/16, Department of Economics, University of Leicester. [Downloadable!]
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  3. Jean Boivin & Serena Ng, 2003. "Are More Data Always Better for Factor Analysis?," NBER Working Papers 9829, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  4. David H. Small & Domenico Giannone & Lucrezia Reichlin, 2006. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Working Paper Series 633, European Central Bank. [Downloadable!]
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  5. Marie Diron, 2006. "Short-term forecasts of euro area real GDP growth - an assessment of real-time performance based on vintage data," Working Paper Series 622, European Central Bank. [Downloadable!]
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  6. Min, Chung-ki & Zellner, Arnold, 1993. "Bayesian and non-Bayesian methods for combining models and forecasts with applications to forecasting international growth rates," Journal of Econometrics, Elsevier, vol. 56(1-2), pages 89-118, March. [Downloadable!] (restricted)
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  7. Ravi Jagannathan & Tongshu Ma, 2003. "Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps," Journal of Finance, American Finance Association, vol. 58(4), pages 1651-1684, 08. [Downloadable!] (restricted)
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  8. Massimiliano Marcellino, 2004. "Forecast Pooling for European Macroeconomic Variables," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(1), pages 91-112, 02. [Downloadable!] (restricted)
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