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The accuracy and efficiency of the Consensus Forecasts: A further application and extension of the pooled approach

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  • Ager, P.
  • Kappler, M.
  • Osterloh, S.

Abstract

This paper analyses the performance of consensus forecasts, published by Consensus Economics, for 12 countries over the period from 1996 to 2006 regarding bias and information efficiency. A pooled approach is employed which permits the evaluation of all forecasts for each target variable over 24 horizons simultaneously. It is shown how the pooled approach needs to be set up in order to accommodate the forecasting scheme of the consensus forecasts. Furthermore, the pooled approach is extended by a sequential test for detecting the critical horizon after which the forecast should be regarded as biased. Moreover, heteroscedasticity in the form of target-year-specific variances of macroeconomic shocks is taken into account. The results show that in the analysed period, which was characterised by pronounced macroeconomic shocks, several countries show biased forecasts, especially with forecast horizons of more than 12 months. In addition, information efficiency has to be rejected in almost all cases.

Suggested Citation

  • Ager, P. & Kappler, M. & Osterloh, S., 2009. "The accuracy and efficiency of the Consensus Forecasts: A further application and extension of the pooled approach," International Journal of Forecasting, Elsevier, vol. 25(1), pages 167-181.
  • Handle: RePEc:eee:intfor:v:25:y:2009:i:1:p:167-181
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    More about this item

    Keywords

    Evaluating forecasts Business cycle forecasting Inflation forecasting Consensus forecasts Bias and efficiency;

    JEL classification:

    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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