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Robustifying Forecasts from Equilibrium-Correction Models

  • David F. Hendry

    ()

    (Economcis Department, University of Oxford)

In a non-stationary world subject to structural breaks, where model and mechanism differ, equilibrium-correction models are a risky device from which to forecast. Equilibrium shifts entail systematic forecast failure, and indeed forecasts will tend to move in the opposite direction to the data. A new explanation for the empirical success of second differencing is proposed. We consider model transformations based on additional differencing to reduce forecast-error biases, as usual at some cost in increased forecast-error variances. The analysis is illustrated by an empirical application to narrow money holdings in the UK.

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File URL: http://www.nuff.ox.ac.uk/economics/papers/2004/w14/DFHEqCMRobust.pdf
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Paper provided by Economics Group, Nuffield College, University of Oxford in its series Economics Papers with number 2004-W14.

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Length: 30 pages
Date of creation: 01 Apr 2004
Date of revision:
Handle: RePEc:nuf:econwp:0414
Contact details of provider: Web page: https://www.nuffield.ox.ac.uk/economics/

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  1. David Hendry & Michael P. Clements, 2001. "Economic Forecasting: Some Lessons from Recent Research," Economics Papers 2002-W11, Economics Group, Nuffield College, University of Oxford.
  2. Hendry, David F. & Ericsson, Neil R., 1991. "Modeling the demand for narrow money in the United Kingdom and the United States," European Economic Review, Elsevier, vol. 35(4), pages 833-881, May.
  3. 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.
  4. Bontemps, Christophe & Mizon, Grayham E., 2001. "Congruence and encompassing," Discussion Paper Series In Economics And Econometrics 0107, Economics Division, School of Social Sciences, University of Southampton.
  5. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521634809, June.
  6. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
  7. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," Review of Economic Studies, Oxford University Press, vol. 65(3), pages 361-393.
  8. Francis X. Diebold & Jose A. Lopez, 1995. "Forecast evaluation and combination," Research Paper 9525, Federal Reserve Bank of New York.
  9. Mario Forni & Marc Hallin & Lucrezia Reichlin & Marco Lippi, 2000. "The generalised dynamic factor model: identification and estimation," ULB Institutional Repository 2013/10143, ULB -- Universite Libre de Bruxelles.
  10. repec:cup:etheor:v:8:y:1992:i:2:p:188-202 is not listed on IDEAS
  11. Melino, Angelo & Turnbull, Stuart M., 1990. "Pricing foreign currency options with stochastic volatility," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 239-265.
  12. 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.
  13. Paruolo, Paolo, 1996. "On the determination of integration indices in I(2) systems," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 313-356.
  14. Michael P. Clements & David F. Hendry, 2001. "Forecasting Non-Stationary Economic Time Series," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262531895.
  15. Hendry, David F., 1995. "Dynamic Econometrics," OUP Catalogue, Oxford University Press, number 9780198283164, June.
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