Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/152630
Authors: 
Gonçalves, Sílvia
Kilian, Lutz
Year of Publication: 
2002
Series/Report no.: 
ECB Working Paper 196
Abstract: 
Conditional heteroskedasticity is an important feature of many macroeconomic and financial time series. Standard residual-based bootstrap procedures for dynamic regression models treat the regression eroor as i.i.d. These procedures are invalid in the presence of conditional heteroskedasticity. We establish the asymptotic validity of three easy-to-implement alternative bootstrap proposals for stationary autoregressive processes with m.d.s. errors subject to possible conditional heteroskedasticity of unknown form. These proposals are the fixed-design wild bootstrap, the recursive design wild bootstrap and the pairwise bootstrap. In a simulation study all three procedures tend to be more accurate in small samples than the conventional large-sample approximation based on robust standard errors. In contrast, standard residual-based bootstrap methods for models with i.i.d. errors may be very inaccurate if the i.i.d. assumption is violated. We conclude that in many empirical applications the proposed robust bootstrap procedures should routinely replace conventional bootstrap procedures based on the i.i.d. error assumption.
Subjects: 
GARCH
pairwise bootstrap
robust inference
stochastic volatility
wild bootstrap
JEL: 
C15
C22
C52
Document Type: 
Working Paper

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