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Random matrix theory and the failure of macro-economic forecasts

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  • Ormerod, Paul
  • Mounfield, Craig

Abstract

By scientific standards, the accuracy of short-term economic forecasts has been poor, and shows no sign of improving over time. We form a delay matrix of time-series data on the overall rate of growth of the economy, with lags spanning the period over which any regularity of behaviour is postulated by economists to exist. We use methods of random matrix theory to analyse the correlation matrix of the delay matrix. This is done for annual data from 1871 to 1994 for 17 economies, and for post-war quarterly data for the US and the UK. The properties of the eigenvalues and eigenvectors of these correlation matrices are similar, though not identical, to those implied by random matrix theory. This suggests that the genuine information content in economic growth data is low, and so forecasting failure arises from inherent properties of the data.

Suggested Citation

  • Ormerod, Paul & Mounfield, Craig, 2000. "Random matrix theory and the failure of macro-economic forecasts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 280(3), pages 497-504.
    • Handle: RePEc:eee:phsmap:v:280:y:2000:i:3:p:497-504
    DOI: 10.1016/S0378-4371(00)00075-3
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    References listed on IDEAS

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    Cited by:

    1. Ormerod, Paul & Mounfield, Craig, 2002. "The convergence of European business cycles 1978–2000," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 307(3), pages 494-504.
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    4. Ormerod, Paul, 2008. "Random Matrix Theory and Macro-Economic Time-Series: An Illustration Using the Evolution of Business Cycle Synchronisation, 1886-2006," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy, vol. 2, pages 1-10.
    5. Duc Thi Luu, 2022. "Portfolio Correlations in the Bank-Firm Credit Market of Japan," Computational Economics, Springer;Society for Computational Economics, vol. 60(2), pages 529-569, August.

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