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Unpredictability and the Foundations of Economic Forecasting

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  • David F. Hendry

Abstract

We revisit the concept of unpredictability to explore its implications for forecasting strategies in a non-stationary world subject to structural breaks, where model and mechanism differ. Six aspects of the role of unpredictability are distinguished, compounding the four additional mistakes most likely in estimated forecasting models. Structural breaks, rather than limited information, are the key problem, exacerbated by conflicting requirements on `forecast-error corrections'. We consider model transformations and corrections to reduce forecast-error biases, as usual at some cost in increased forecast-error variances. The analysis is illustrated by an empirical application to M1 in the UK

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Bibliographic Info

Paper provided by Econometric Society in its series Econometric Society 2004 Australasian Meetings with number 27.

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Date of creation: 11 Aug 2004
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Handle: RePEc:ecm:ausm04:27

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Keywords: Forecasting; Unpredictability;

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References

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  1. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
  2. David Hendry & Michael P. Clements, 2001. "Economic Forecasting: Some Lessons from Recent Research," Economics Series Working Papers 78, University of Oxford, Department of Economics.
  3. Kim, Sangjoon & Shephard, Neil & Chib, Siddhartha, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," Review of Economic Studies, Wiley Blackwell, vol. 65(3), pages 361-93, July.
  4. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521632423, Fall.
  5. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
  6. David Hendry & Guillaume Chevillon, 2004. "Non-Parametric Direct Multi-step Estimation for Forecasting Economic Processes," Economics Series Working Papers 196, University of Oxford, Department of Economics.
  7. repec:cup:etheor:v:8:y:1992:i:2:p:188-202 is not listed on IDEAS
  8. Clara Jørgensen & Hans Christian Kongsted & Anders Rahbek, 1996. "Trend-Stationarity in the I(2) Cointegration Model," Discussion Papers 96-12, University of Copenhagen. Department of Economics.
  9. Paruolo, Paolo, 1996. "On the determination of integration indices in I(2) systems," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 313-356.
  10. David F. Hendry & Neil R. Ericsson, 1990. "Modeling the demand for narrow money in the United Kingdom and the United States," International Finance Discussion Papers 383, Board of Governors of the Federal Reserve System (U.S.).
  11. Francis X. Diebold & Jose A. Lopez, 1995. "Forecast evaluation and combination," Research Paper 9525, Federal Reserve Bank of New York.
  12. Johansen, Søren, 1992. "A Representation of Vector Autoregressive Processes Integrated of Order 2," Econometric Theory, Cambridge University Press, vol. 8(02), pages 188-202, June.
  13. Hendry, David F., 1995. "Dynamic Econometrics," OUP Catalogue, Oxford University Press, number 9780198283164, Octomber.
  14. Melino, Angelo & Turnbull, Stuart M., 1990. "Pricing foreign currency options with stochastic volatility," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 239-265.
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Cited by:
  1. Hendry, David F & Hubrich, Kirstin, 2006. "Forecasting Economic Aggregates by Disaggregates," CEPR Discussion Papers 5485, C.E.P.R. Discussion Papers.
  2. Kirstin Hubrich & David F. Hendry, 2005. "Forecasting Aggregates by Disaggregates," Computing in Economics and Finance 2005 270, Society for Computational Economics.
  3. Qin, Duo & Cagas, Marie Anne & Ducanes, Geoffrey & Magtibay-Ramos, Nedelyn & Quising, Pilipinas, 2008. "Automatic leading indicators versus macroeconometric structural models: A comparison of inflation and GDP growth forecasting," International Journal of Forecasting, Elsevier, vol. 24(3), pages 399-413.

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