1. Local Bootstrap Approach for the Estimation of the Memory Parameter.
- Author
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Arteche, J. and Orbe, J.
- Subjects
- *
STATISTICAL bootstrapping , *CONFIDENCE intervals , *STATISTICAL hypothesis testing , *NONPARAMETRIC statistics , *REGRESSION analysis - Abstract
The log periodogram regression is widely used in empirical applications because of its simplicity to estimate the memory parameter, d, its good asymptotic properties and its robustness to misspecification of the short term behavior of the series. However, the asymptotic distribution is a poor approximation of the (unknown) finite sample distribution if the sample size is small. Here the finite sample performance of different nonparametric residual bootstrap procedures is analyzed when applied to construct confidence intervals. In particular, in addition to the basic residual bootstrap the local bootstrap that might adequately replicate the structure that may arise in the errors of the regression is considered when the series shows weak dependence in addition to the long memory component. Bias correcting bootstrap to adjust the bias caused by that structure is also considered. [ABSTRACT FROM AUTHOR]
- Published
- 2009