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Non-Parametric Direct Multi-step Estimation for Forecasting Economic Processes

  • David Hendry
  • Guillaume Chevillon

We evaluate the asymptotic and finite-sample properties of direct multi-step estimation (DMS) for forecasting at several horizons. For forecast accuracy gains from DMS in finite samples, mis-specification and non-stationarity of the DGP are necessary, but when a model is well-specified, iterating the one-step ahead froecasts may not be asymptotically preferable. If a model is mis-specified for a non-stationary DGP, in particular omitting either negative residual serial correlation or regime shifts, DMS can forecast more accurately. Monte Carlo simulations clarify the non-linear dependence of the estimation and forecast biases on the parameters of the DGP, and explain existing results.

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File URL: http://www.economics.ox.ac.uk/materials/working_papers/paper196.pdf
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Paper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 196.

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Date of creation: 01 Jul 2004
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Handle: RePEc:oxf:wpaper:196
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  1. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
  2. Weiss, Andrew A., 1991. "Multi-step estimation and forecasting in dynamic models," Journal of Econometrics, Elsevier, vol. 48(1-2), pages 135-149.
  3. 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.
  4. Clements, Michael P & Hendry, David F, 1996. "Multi-step Estimation for Forecasting," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 58(4), pages 657-84, November.
  5. 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, June.
  6. Banerjee, A & Hendry, D-F & Mizon, G-E, 1996. "The Econometric Analysis of Economic Policy," Economics Working Papers eco96/34, European University Institute.
  7. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
  8. Hendry, David F., 2000. "On detectable and non-detectable structural change," Structural Change and Economic Dynamics, Elsevier, vol. 11(1-2), pages 45-65, July.
  9. Johnston, H N, 1974. "A Note on the Estimation and Prediction Inefficiency of "Dynamic" Estimators," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 15(1), pages 251-55, February.
  10. Kang, In-Bong, 2003. "Multi-period forecasting using different models for different horizons: an application to U.S. economic time series data," International Journal of Forecasting, Elsevier, vol. 19(3), pages 387-400.
  11. Fildes, Robert & Stekler, Herman, 2002. "The state of macroeconomic forecasting," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 435-468, December.
  12. Fildes, Robert & Stekler, Herman, 2002. "Reply to the comments on 'The state of macroeconomic forecasting'," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 503-505, December.
  13. R. Bhansali, 1996. "Asymptotically efficient autoregressive model selection for multistep prediction," Annals of the Institute of Statistical Mathematics, Springer, vol. 48(3), pages 577-602, September.
  14. Ing, Ching-Kang, 2003. "Multistep Prediction In Autoregressive Processes," Econometric Theory, Cambridge University Press, vol. 19(02), pages 254-279, April.
  15. James H. Stock & Mark W. Watson, 1998. "A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series," NBER Working Papers 6607, National Bureau of Economic Research, Inc.
  16. Hendry, David F, 1991. "Using PC-NAIVE in Teaching Econometrics," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 53(2), pages 199-223, May.
  17. Clements, Michael P. & Hendry, David F., 1998. "Forecasting economic processes," International Journal of Forecasting, Elsevier, vol. 14(1), pages 111-131, March.
  18. Magnus, Jan R. & Pesaran, Bahram, 1989. "The exact multi-period mean-square forecast error for the first-order autoregressive model with an intercept," Journal of Econometrics, Elsevier, vol. 42(2), pages 157-179, October.
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