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Empirical simultaneous prediction regions for path-forecasts

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  • Jordà, Òscar
  • Knüppel, Malte
  • Marcellino, Massimiliano

Abstract

This paper investigates the problem of constructing prediction regions for forecast trajectories 1 to H periods into the future—a path forecast. When the null model is only approximative, or completely unavailable, one cannot either derive the usual analytic expressions or resample from the null model. In this context, this paper derives a method for constructing approximate rectangular regions for simultaneous probability coverage that correct for serial correlation in the case of elliptical distributions. In both Monte Carlo studies and an empirical application to the Greenbook path-forecasts of growth and inflation, the performance of this method is compared to the performances of the Bonferroni approach and the approach which ignores simultaneity.

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Bibliographic Info

Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 29 (2013)
Issue (Month): 3 ()
Pages: 456-468

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Handle: RePEc:eee:intfor:v:29:y:2013:i:3:p:456-468

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Web page: http://www.elsevier.com/locate/ijforecast

Related research

Keywords: Path-forecast; Forecast uncertainty; Simultaneous prediction region; Scheffé’s S-method; Mahalanobis distance;

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  1. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521634809, April.
  2. Anna Staszewska-Bystrova, 2013. "Modified Scheffé’s Prediction Bands," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 233(5-6), pages 680-690, October.
  3. Jordà, Òscar & Marcellino, Massimiliano, 2008. "Path Forecast Evaluation," CEPR Discussion Papers 7009, C.E.P.R. Discussion Papers.
  4. Margaret McConnell & Gabriel Perez Quiros, 2000. "Output fluctuations in the United States: what has changed since the early 1980s?," Proceedings, Federal Reserve Bank of San Francisco, issue Mar.
  5. 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.
  6. James H. Stock & Mark W. Watson, 2001. "Vector Autoregressions," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 101-115, Fall.
  7. Jushan Bai & Serena Ng, 2001. "Tests for Skewness, Kurtosis, and Normality for Time Series Data," Boston College Working Papers in Economics 501, Boston College Department of Economics.
  8. Clements, Michael P. & Taylor, Nick, 2001. "Bootstrapping prediction intervals for autoregressive models," International Journal of Forecasting, Elsevier, vol. 17(2), pages 247-267.
  9. Lorenzo Pascual & Juan Romo & Esther Ruiz, 2004. "Bootstrap predictive inference for ARIMA processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(4), pages 449-465, 07.
  10. Masarotto, Guido, 1990. "Bootstrap prediction intervals for autoregressions," International Journal of Forecasting, Elsevier, vol. 6(2), pages 229-239, July.
  11. 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.
  12. Kim, Jae H., 1999. "Asymptotic and bootstrap prediction regions for vector autoregression," International Journal of Forecasting, Elsevier, vol. 15(4), pages 393-403, October.
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