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A new theory of forecasting

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  • Manganelli, Simone

Abstract

This paper argues that forecast estimators should minimise the loss function in a statistical, rather than deterministic, way. We introduce two new elements into the classical econometric analysis: a subjective guess on the variable to be forecasted and a probability reflecting the confidence associated to it. We then propose a new forecast estimator based on a test of whether the first derivatives of the loss function evaluated at the subjective guess are statistically different from zero. We show that the classical estimator is a special case of this new estimator, and that in general the two estimators are asymptotically equivalent. We illustrate the implications of this new theory with a simple simulation, an application to GDP forecast and an example of mean-variance portfolio selection. JEL Classification: C13, C53, G11

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Bibliographic Info

Paper provided by European Central Bank in its series Working Paper Series with number 0584.

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Date of creation: Jan 2006
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Handle: RePEc:ecb:ecbwps:20060584

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Related research

Keywords: asset allocation; Decision under uncertainty; estimation; overfitting;

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References

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  1. Elliott, Graham & Timmermann, Allan G, 2007. "Economic Forecasting," CEPR Discussion Papers 6158, C.E.P.R. Discussion Papers.
  2. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-38, May.
  3. Simone Manganelli, 2004. "Asset Allocation by Variance Sensitivity Analysis," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 2(3), pages 370-389.
  4. Newey, Whitney K. & McFadden, Daniel, 1986. "Large sample estimation and hypothesis testing," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 36, pages 2111-2245 Elsevier.
  5. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
  6. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
  7. Granger, Clive W.J. & Machina, Mark J., 2006. "Forecasting and Decision Theory," Handbook of Economic Forecasting, Elsevier.
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Cited by:
  1. Andrés González & Kirstin Hubrich & Timo Teräsvirta, 2009. "Forecasting inflation with gradual regime shifts and exogenous information," CREATES Research Papers 2009-03, School of Economics and Management, University of Aarhus.
  2. Daniel Dias & Carlos Robalo Marques & J.M.C.Santos Silva, 2006. "Measuring the Importance of the Uniform Nonsynchronization Hypothesis," Working Papers w200603, Banco de Portugal, Economics and Research Department.

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