Recent developments in bootstrap methods for dependent data
Dimitris N. Politis,
Silvia Goncalves () and
Benoit Perron ()
Journal of Time Series Analysis, 2015, vol. 36, issue 3, 481-502
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.
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