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On winning forecasting competitions in economics

Listed author(s):
  • Michael P. Clements

    (Economics Department, Warwick University, Coventry, CV4 7AL, UK Nuffield College, Oxford, OX1 1NF, UK)

  • David F. Hendry

    (Economics Department, Warwick University, Coventry, CV4 7AL, UK Nuffield College, Oxford, OX1 1NF, UK)

To explain which methods might win forecasting competitions on economic time series, we consider forecasting in an evolving economy subject to structural breaks, using mis-specified, data-based models. `Causal' models need not win when facing deterministic shifts, a primary factor underlying systematic forecast failure. We derive conditional forecast biases and unconditional (asymptotic) variances to show that when the forecast evaluation sample includes sub-periods following breaks, non-causal models will outperform at short horizons. This suggests using techniques which avoid systematic forecasting errors, including improved intercept corrections. An application to a small monetary model of the UK illustrates the theory.

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Article provided by Springer & Spanish Economic Association in its journal Spanish Economic Review.

Volume (Year): 1 (1999)
Issue (Month): 2 ()
Pages: 123-160

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Handle: RePEc:spr:specre:v:1:y:1999:i:2:p:123-160
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