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Bias-corrected bootstrap prediction regions for vector autoregression

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Author Info

  • Jae H. Kim

    (Department of Econometrics and Business Statistics, Monash University, Caulfield East, Victoria 3145, Australia)

Abstract

This paper examines small sample properties of alternative bias-corrected bootstrap prediction regions for the vector autoregressive (VAR) model. Bias-corrected bootstrap prediction regions are constructed by combining bias-correction of VAR parameter estimators with the bootstrap procedure. The backward VAR model is used to bootstrap VAR forecasts conditionally on past observations. Bootstrap prediction regions based on asymptotic bias-correction are compared with those based on bootstrap bias-correction. Monte Carlo simulation results indicate that bootstrap prediction regions based on asymptotic bias-correction show better small sample properties than those based on bootstrap bias-correction for nearly all cases considered. The former provide accurate coverage properties in most cases, while the latter over-estimate the future uncertainty. Overall, the percentile-t bootstrap prediction region based on asymptotic bias-correction is found to provide highly desirable small sample properties, outperforming its alternatives in nearly all cases. Copyright © 2004 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/for.908
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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 23 (2004)
Issue (Month): 2 ()
Pages: 141-154

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Handle: RePEc:jof:jforec:v:23:y:2004:i:2:p:141-154

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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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Cited by:
  1. Helmut Lütkepohl & Anna Staszewska-Bystrova & Peter Winker, 2014. "Confidence Bands for Impulse Responses: Bonferroni versus Wald," SFB 649 Discussion Papers SFB649DP2014-007, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  2. Helmut Lütkepohl & Anna Staszewska-Bystrova & Peter Winker, 2013. "Comparison of Methods for Constructing Joint Confidence Bands for Impulse Response Functions," Discussion Papers of DIW Berlin 1292, DIW Berlin, German Institute for Economic Research.
  3. Staszewska-Bystrova, Anna & Winker, Peter, 2013. "Constructing narrowest pathwise bootstrap prediction bands using threshold accepting," International Journal of Forecasting, Elsevier, vol. 29(2), pages 221-233.
  4. Tom Engsted & Thomas Q. Pedersen, 2014. "Bias-Correction in Vector Autoregressive Models: A Simulation Study," Econometrics, MDPI, Open Access Journal, vol. 2(1), pages 45-71, March.
  5. Liu, Shen & Maharaj, Elizabeth Ann & Inder, Brett, 2014. "Polarization of forecast densities: A new approach to time series classification," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 345-361.
  6. Anna Staszewska‐Bystrova, 2011. "Bootstrap prediction bands for forecast paths from vector autoregressive models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(8), pages 721-735, December.

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