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Recent developments in bootstrap methods for dependent data

Author

Listed:
  • Giuseppe Cavaliere
  • Dimitris N. Politis
  • Anders Rahbek
  • Marco Meyer
  • Jens-Peter Kreiss

Abstract

type="main" xml:id="jtsa12090-abs-0001"> The concept of autoregressive sieve bootstrap is investigated for the case of vector autoregressive (VAR) time series. This procedure fits a finite-order VAR model to the given data and generates residual-based bootstrap replicates of the time series. The paper explores the range of validity of this resampling procedure and provides a general check criterion, which allows to decide whether the VAR sieve bootstrap asymptotically works for a specific statistic or not. In the latter case, we will point out the exact reason that causes the bootstrap to fail. The developed check criterion is then applied to some particularly interesting statistics.

Suggested Citation

  • Giuseppe Cavaliere & Dimitris N. Politis & Anders Rahbek & Marco Meyer & Jens-Peter Kreiss, 2015. "Recent developments in bootstrap methods for dependent data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(3), pages 377-397, May.
    • Handle: RePEc:bla:jtsera:v:36:y:2015:i:3:p:377-397
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    References listed on IDEAS

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    1. Romano, Joseph P. & Wolf, Michael, 2000. "A more general central limit theorem for m-dependent random variables with unbounded m," Statistics & Probability Letters, Elsevier, vol. 47(2), pages 115-124, April.
    2. Paparoditis, Efstathios, 1996. "Bootstrapping Autoregressive and Moving Average Parameter Estimates of Infinite Order Vector Autoregressive Processes," Journal of Multivariate Analysis, Elsevier, vol. 57(2), pages 277-296, May.
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    Cited by:

    1. Germán Aneiros & Paula Raña & Philippe Vieu & Juan Vilar, 2018. "Bootstrap in semi-functional partial linear regression under dependence," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 659-679, September.

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