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Predicting GDP Components. Do Leading Indicators Increase Predictability?

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Author Info
Jonas Dovern

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Abstract

We use the concept of predictability as presented in Diebold and Kilian (2001) to assess how well the growth rates of various components of German GDP can be forecasted. In particular, it is analyzed how well different commonly used leading indicators can increase predictability of these time series. To this end, we propose an algorithm to select an optimal information set from a full set of possible leading indicators. In the univariate set up, we find very small degrees of predictability for all quarterly growth rates whereas yearly growth rates seem to be more predictable at short forecast horizons. According to the algorithm proposed, from a set of financial leading indicators the short term interest rate is included in the highest number of information sets and from a set of survey indicators the ifo-business expectation index is included in most cases. Conditioning on the optimal sets of leading indicators improves the predictability of most of the quarterly growth rates substantially while the predictabilities of the yearly growth rates cannot be increased significantly further. The results indicate that there is clearly evidence that complicated forecasting models are usually superior to simple AR univariate models.

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Paper provided by Kiel Institute for the World Economy in its series Kiel Advanced Studies Working Papers with number 436.

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Length: 27 pages
Date of creation: Jul 2006
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Handle: RePEc:kie:kieasw:436

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Keywords: Predictability Leading Indicators GDP component

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Find related papers by JEL classification:
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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    Other versions:
  4. Banerjee, Anindya & Marcellino, Massimiliano & Masten, Igor, 2003. "Leading Indicators for Euro Area Inflation and GDP Growth," CEPR Discussion Papers 3893, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
    Other versions:
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    Other versions:
  9. Galbraith, John W. & KI[#x1e63]Inbay, Turgut, 2005. "Content horizons for conditional variance forecasts," International Journal of Forecasting, Elsevier, vol. 21(2), pages 249-260. [Downloadable!] (restricted)
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    Other versions:
  14. Konstantin A. Kholodilin & Boriss Siliverstovs, 2005. "On the Forecasting Properties of the Alternative Leading Indicators for the German GDP : Recent Evidence," Discussion Papers of DIW Berlin 522, DIW Berlin, German Institute for Economic Research. [Downloadable!]
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    Other versions:
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