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Bootstrap confidence regions computed from autoregressions of arbitrary order

Author

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  • Edwin Choi
  • Peter Hall

Abstract

Given a linear time series, e.g. an autoregression of infinite order, we may construct a finite order approximation and use that as the basis for confidence regions. The sieve or autoregressive bootstrap, as this method is often called, is generally seen as a competitor with the better‐understood block bootstrap approach. However, in the present paper we argue that, for linear time series, the sieve bootstrap has significantly better performance than blocking methods and offers a wider range of opportunities. In particular, since it does not corrupt second‐order properties then it may be used in a double‐bootstrap form, with the second bootstrap application being employed to calibrate a basic percentile method confidence interval. This approach confers second‐order accuracy without the need to estimate variance. That offers substantial benefits, since variances of statistics based on time series can be difficult to estimate reliably, and—partly because of the relatively small amount of information contained in a dependent process—are notorious for causing problems when used to Studentize. Other advantages of the sieve bootstrap include considerably greater robustness against variations in the choice of the tuning parameter, here equal to the autoregressive order, and the fact that, in contradistinction to the case of the block bootstrap, the percentile t version of the sieve bootstrap may be based on the ‘raw'’ estimator of standard error. In the process of establishing these properties we show that the sieve bootstrap is second order correct.

Suggested Citation

  • Edwin Choi & Peter Hall, 2000. "Bootstrap confidence regions computed from autoregressions of arbitrary order," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(2), pages 461-477.
  • Handle: RePEc:bla:jorssb:v:62:y:2000:i:2:p:461-477
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    File URL: https://doi.org/10.1111/1467-9868.00244
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    Cited by:

    1. Joel L. Horowitz, 2018. "Bootstrap Methods in Econometrics," Papers 1809.04016, arXiv.org.
    2. Philip Preuss & Ruprecht Puchstein & Holger Dette, 2015. "Detection of Multiple Structural Breaks in Multivariate Time Series," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 654-668, June.
    3. Poskitt, D.S. & Grose, Simone D. & Martin, Gael M., 2015. "Higher-order improvements of the sieve bootstrap for fractionally integrated processes," Journal of Econometrics, Elsevier, vol. 188(1), pages 94-110.
    4. Psaradakis, Zacharias & Vávra, Marián, 2017. "A distance test of normality for a wide class of stationary processes," Econometrics and Statistics, Elsevier, vol. 2(C), pages 50-60.
    5. Pesaran, Mohammad Hashem & Holly, Sean & Dees, Stephane & Smith, L. Vanessa, 2007. "Long Run Macroeconomic Relations in the Global Economy," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 1, pages 1-20.
    6. Valeria D’Amato & Steven Haberman & Gabriella Piscopo & Maria Russolillo, 2014. "Computational framework for longevity risk management," Computational Management Science, Springer, vol. 11(1), pages 111-137, January.
    7. Härdle, Wolfgang & Horowitz, Joel L. & Kreiss, Jens-Peter, 2001. "Bootstrap methods for time series," SFB 373 Discussion Papers 2001,59, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    8. Psaradakis, Zacharias, 2003. "A sieve bootstrap test for stationarity," Statistics & Probability Letters, Elsevier, vol. 62(3), pages 263-274, April.
    9. Neil Kellard & Denise Osborn & Jerry Coakley & Simone D. Grose & Gael M. Martin & Donald S. Poskitt, 2015. "Bias Correction of Persistence Measures in Fractionally Integrated Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(5), pages 721-740, September.
    10. D’Amato, Valeria & Haberman, Steven & Piscopo, Gabriella & Russolillo, Maria, 2012. "Modelling dependent data for longevity projections," Insurance: Mathematics and Economics, Elsevier, vol. 51(3), pages 694-701.
    11. D.S. Poskitt & Gael M. Martin & Simone D. Grose, 2012. "Bias Reduction of Long Memory Parameter Estimators via the Pre-filtered Sieve Bootstrap," Monash Econometrics and Business Statistics Working Papers 8/12, Monash University, Department of Econometrics and Business Statistics.
    12. Joel L. Horowitz, 2018. "Bootstrap methods in econometrics," CeMMAP working papers CWP53/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

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