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

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Author Info
Guillaume Chevillon
David Hendry

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Abstract

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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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: 2004
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Handle: RePEc:oxf:wpaper:196

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Keywords: Adaptive Estimation Multi-Step Estimation Dynamic Forecasts Model Mis-Specification

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Find related papers by JEL classification:
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models
C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications

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  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. Newey, Whitney K & West, Kenneth D, 1987. "A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix," Econometrica, Econometric Society, vol. 55(3), pages 703-08, May. [Downloadable!] (restricted)
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  3. Fildes, Robert & Stekler, Herman, 2002. "The state of macroeconomic forecasting," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 435-468, December. [Downloadable!] (restricted)
  4. 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)
  5. 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. [Downloadable!] (restricted)
  6. 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. [Downloadable!] (restricted)
  7. Ing, Ching-Kang, 2003. "Multistep Prediction In Autoregressive Processes," Econometric Theory, Cambridge University Press, vol. 19(02), pages 254-279, January. [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. 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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  10. Banerjee, A & Hendry, D-F & Mizon, G-E, 1996. "The Econometric Analysis of Economic Policy," Economics Working Papers eco96/34, European University Institute.
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  11. Clements, Michael P. & Hendry, David F., 1998. "Forecasting economic processes," International Journal of Forecasting, Elsevier, vol. 14(1), pages 111-131, March. [Downloadable!] (restricted)
  12. 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. [Downloadable!] (restricted)
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Cited by:
(explanations, 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. Todd E. Clark & Michael W. McCracken, 2001. "Evaluating long-horizon forecasts," Research Working Paper RWP 01-14, Federal Reserve Bank of Kansas City. [Downloadable!]
  2. Guillaume Chevillon, 2004. "A Comparison of Multi-step GDP Forecasts for South Africa," Economics Series Working Papers 212, University of Oxford, Department of Economics. [Downloadable!]
    Other versions:
  3. Massimiliano Marcellino & Christian Schumacher, . "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP1," Working Papers 333, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University. [Downloadable!]
    Other versions:
  4. Guillaume Chevillon, 2004. "`Weak` trends for inference and forecasting in finite samples," Economics Series Working Papers 210, University of Oxford, Department of Economics. [Downloadable!]
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  5. Eliana González Molano & Luis Fernando Melo Velandia & Anderson Grajales Olarte, . "Pronósticos directos de la inflación colombiana," Borradores de Economia 458, Banco de la Republica de Colombia. [Downloadable!]
    Other versions:
  6. Eklund, Jana & Karlsson, Sune, 2005. "Forecast Combination and Model Averaging using Predictive Measures," Working Paper Series 191, Sveriges Riksbank (Central Bank of Sweden). [Downloadable!]
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  7. Alfred A Haug & Christie Smith, 2007. "Local linear impulse responses for a small open economy," Reserve Bank of New Zealand Discussion Paper Series DP2007/09, Reserve Bank of New Zealand. [Downloadable!]
  8. Jennifer L. Castle & David F. Hendry, 2007. "Forecasting UK Inflation: the Roles of Structural Breaks and Time Disaggregation," Economics Series Working Papers 309, University of Oxford, Department of Economics. [Downloadable!]
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