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Evaluating CPB’s published GDP growth forecasts

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Author Info
Adam Elbourne ()
Henk Kranendonk ()
Rob Luginbuhl ()
Bert Smid ()
Martin Vromans ()

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Abstract

We compare the accuracy of our published GDP growth forecasts from our large macro model, SAFFIER, to those produced by VAR based models using both classical and Bayesian estimation techniques. We employ a data driven methodology for selecting variables to include in our VAR models and we find that a randomly selected classical VAR model performs worse in most cases than the Bayesian equivalent, which performs worse than our published forecasts in most cases. However, when we pool forecasts across many VARs we can produce more accurate forecasts than we published. A review of the literature suggests that forecast accuracy is likely irrelevant for the non-forecasting activities the model is used for at CPB because they are fundamentally different activities.

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File URL: http://www.cpb.nl/eng/pub/cpbreeksen/document/172/doc172.pdf
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Publisher Info
Paper provided by CPB Netherlands Bureau for Economic Policy Analysis in its series CPB Documents with number 172.

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Date of creation: Oct 2008
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Handle: RePEc:cpb:docmnt:172

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Related research
Keywords: SEMs; VAR models; Forecast combination; Bayesian methods; Real time;

Find related papers by JEL classification:
C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation

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