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Cointegration and long-horizon forecasting

  • Peter F. Christoffersen
  • Francis X. Diebold

It is widely believed that imposing cointegration on a forecasting system, if cointegration is, in fact, present, will improve long-horizon forecasts. The authors show that, contrary to this belief, at long horizons nothing is lost by ignoring cointegration when the forecasts are evaluated using standard multivariate forecast accuracy measures. In fact, simple univariate Box-Jenkins forecasts are just as accurate. The authors' results highlight a potentially important deficiency of standard forecast accuracy measures--they fail to value the maintenance of cointegrating relationships among variables--and the authors suggest alternatives that explicitly do so.

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Paper provided by Federal Reserve Bank of Philadelphia in its series Working Papers with number 97-14.

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Date of creation: 1997
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Handle: RePEc:fip:fedpwp:97-14
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  1. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
  2. Campbell, John & Shiller, Robert, 1987. "Cointegration and Tests of Present Value Models," Scholarly Articles 3122490, Harvard University Department of Economics.
  3. Phillips, P C B, 1991. "Optimal Inference in Cointegrated Systems," Econometrica, Econometric Society, vol. 59(2), pages 283-306, March.
  4. Francis X. Diebold & Jose A. Lopez, 1995. "Forecast evaluation and combination," Research Paper 9525, Federal Reserve Bank of New York.
  5. Christoffersen & Diebold, . "Further Results on Forecasting and Model Selection Under Asymmetric Loss," Home Pages _059, University of Pennsylvania.
  6. Clements, M.P. & Hendry, D.F., 1992. "Forecasting in Cointegrated Systems," Economics Series Working Papers 99139, University of Oxford, Department of Economics.
  7. Peter F. Christoffersen & Francis X. Diebold, 1997. "Optimal prediction under asymmetric loss," Working Papers 97-11, Federal Reserve Bank of Philadelphia.
  8. Clements, M.P. & Hendry, D., 1992. "On the Limitations of Comparing Mean Square Forecast Errors," Economics Series Working Papers 99138, University of Oxford, Department of Economics.
  9. Eric Zivot, 1996. "The Power of Single Equation Tests for Cointegration when the Cointegrating Vector is Prespecified," Econometrics 9612001, EconWPA.
  10. Engle, Robert F. & Yoo, Byung Sam, 1987. "Forecasting and testing in co-integrated systems," Journal of Econometrics, Elsevier, vol. 35(1), pages 143-159, May.
  11. Wickens, Michael R., 1996. "Interpreting cointegrating vectors and common stochastic trends," Journal of Econometrics, Elsevier, vol. 74(2), pages 255-271, October.
  12. Granger, Clive W J, 1996. "Can We Improve the Perceived Quality of Economic Forecasts?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(5), pages 455-73, Sept.-Oct.
  13. Hoffman, Dennis L & Rasche, Robert H, 1996. "Assessing Forecast Performance in a Cointegrated System," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(5), pages 495-517, Sept.-Oct.
  14. Lin, Jin-Lung & Tsay, Ruey S, 1996. "Co-integration Constraint and Forecasting: An Empirical Examination," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(5), pages 519-38, Sept.-Oct.
  15. Horvath, Michael T.K. & Watson, Mark W., 1995. "Testing for Cointegration When Some of the Cointegrating Vectors are Prespecified," Econometric Theory, Cambridge University Press, vol. 11(05), pages 984-1014, October.
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