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Direct and iterated multistep AR methods for difference stationary processes

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Author Info
Proietti, Tommaso

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Abstract

The paper focuses on the comparison of the direct and iterated AR predictors for difference stationary processes. In particular, it provides new methods for comparing the efficiency of the two predictors and for extracting the trend from macroeconomic time series using the two methods. The methods are based on an encompassing representation for the two predictors which enables to derive their properties quite easily under a maintained model. The paper provides an analytic expression for the mean square forecast error of the two predictors and derives useful recursive formulae for computing the direct and iterated coefficients. From the empirical standpoint, we propose estimators of the AR coefficients based on the tapered Yule- Walker estimates; we also provide a test of equal forecast accuracy which is very simple to implement and whose critical values are obtained with the bootstrap method.

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File URL: http://mpra.ub.uni-muenchen.de/10859/
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File URL: http://mpra.ub.uni-muenchen.de/15343/
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Publisher Info
Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 10859.

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Date of creation: 01 Oct 2008
Date of revision: 01 Apr 2009
Handle: RePEc:pra:mprapa:10859

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Related research
Keywords: Multistep estimation; Tapered Yule-Walker estimates; Forecast combination.;

Find related papers by JEL classification:
C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Weiss, Andrew A., 1991. "Multi-step estimation and forecasting in dynamic models," Journal of Econometrics, Elsevier, vol. 48(1-2), pages 135-149. [Downloadable!] (restricted)
  2. 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. [Downloadable!] (restricted)
  3. Peter M Robinson & Carlos Velasco, 2000. "Whittle Pseudo-Maximum Likelihood Estimation for Nonstationary Time Series - (Now published in Journal of the American Statistical Association, 95, (2000), pp.1229-1243.)," STICERD - Econometrics Paper Series /2000/391, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE. [Downloadable!]
  4. Clive W. J. Granger & Yongil Jeon, 2006. "Dynamics of Model Overfitting Measured in terms of Autoregressive Roots," Journal of Time Series Analysis, Blackwell Publishing, vol. 27(3), pages 347-365, 05. [Downloadable!] (restricted)
  5. Marcellino, Massimiliano & Stock, James H & Watson, Mark W, 2005. "A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series," CEPR Discussion Papers 4976, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
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  6. Zhou, YanYan & Roy, Anindya, 2006. "Effect of tapering on accuracy of forecasts made with stable estimators of vector autoregressive processes," International Journal of Forecasting, Elsevier, vol. 22(1), pages 169-180. [Downloadable!] (restricted)
  7. Ing, Ching-Kang, 2003. "Multistep Prediction In Autoregressive Processes," Econometric Theory, Cambridge University Press, vol. 19(02), pages 254-279, April. [Downloadable!]
  8. Clements, Michael P. & Hendry, David F., 1996. "Multi-Step Estimation for Forecasting," The Warwick Economics Research Paper Series (TWERPS) 447, University of Warwick, Department of Economics.
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  9. Guillaume Chevillon, 2007. "Direct Multi-Step Estimation And Forecasting," Journal of Economic Surveys, Blackwell Publishing, vol. 21(4), pages 746-785, 09. [Downloadable!] (restricted)
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  10. Proietti, Tommaso & Harvey, Andrew, 2000. "A Beveridge-Nelson smoother," Economics Letters, Elsevier, vol. 67(2), pages 139-146, May. [Downloadable!] (restricted)
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