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Recent developments in bootstrap methods for dependent data

Author

Listed:
  • Giuseppe Cavaliere
  • Dimitris N. Politis
  • Anders Rahbek
  • Antoine Djogbenou
  • Sílvia Gonçalves
  • Benoit Perron

Abstract

type="main" xml:id="jtsa12118-abs-0001"> This article considers bootstrap inference in a factor-augmented regression context where the errors could potentially be serially correlated. This generalizes results in Gonçalves & Perron (2014) and makes the bootstrap applicable to forecasting contexts where the forecast horizon is greater than one. We propose and justify two residual-based approaches, a block wild bootstrap and a dependent wild bootstrap. Our simulations document improvement in coverage rates of confidence intervals for the coefficients when using block wild bootstrap or dependent wild bootstrap relative to both asymptotic theory and the wild bootstrap when serial correlation is present in the regression errors.

Suggested Citation

  • Giuseppe Cavaliere & Dimitris N. Politis & Anders Rahbek & Antoine Djogbenou & Sílvia Gonçalves & Benoit Perron, 2015. "Recent developments in bootstrap methods for dependent data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(3), pages 481-502, May.
    • Handle: RePEc:bla:jtsera:v:36:y:2015:i:3:p:481-502
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    References listed on IDEAS

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