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Bootstrap prediction intervals for single period regression forecasts

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  • Lam, J. -P.
  • Veall, M. R.

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  • Lam, J. -P. & Veall, M. R., 2002. "Bootstrap prediction intervals for single period regression forecasts," International Journal of Forecasting, Elsevier, vol. 18(1), pages 125-130.
    • Handle: RePEc:eee:intfor:v:18:y:2002:i:1:p:125-130
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    References listed on IDEAS

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    1. Masarotto, Guido, 1990. "Bootstrap prediction intervals for autoregressions," International Journal of Forecasting, Elsevier, vol. 6(2), pages 229-239, July.
    2. David M. Prescott & Thanasis Stengos, 1987. "Bootstrapping Confidence Intervals: An Application to Forecasting the Supply of Pork," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 69(2), pages 266-273.
    3. Leslie G. Godfrey & Chris D. Orme, 2000. "Controlling the significance levels of prediction error tests for linear regression models," Econometrics Journal, Royal Economic Society, vol. 3(1), pages 66-83.
    4. 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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    Cited by:

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    2. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    3. Jan G. de Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Tinbergen Institute Discussion Papers 05-068/4, Tinbergen Institute.
    4. Liew, Venus Khim-Sen, 2008. "An overview on various ways of bootstrap methods," MPRA Paper 7163, University Library of Munich, Germany.
    5. Leslie G. Godfrey, 2005. "Controlling the Overall Significance Level of a Battery of Least Squares Diagnostic Tests," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(2), pages 263-279, April.
    6. Bellotti, Tony & Crook, Jonathan, 2012. "Loss given default models incorporating macroeconomic variables for credit cards," International Journal of Forecasting, Elsevier, vol. 28(1), pages 171-182.
    7. Erol Eğrioğlu & Robert Fildes, 2022. "A New Bootstrapped Hybrid Artificial Neural Network Approach for Time Series Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1355-1383, April.
    8. Shmueli, Galit & Ray, Soumya & Velasquez Estrada, Juan Manuel & Chatla, Suneel Babu, 2016. "The elephant in the room: Predictive performance of PLS models," Journal of Business Research, Elsevier, vol. 69(10), pages 4552-4564.

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