@article{(Open Science Index):https://publications.waset.org/pdf/14766,
	  title     = {Small Sample Bootstrap Confidence Intervals for Long-Memory Parameter},
	  author    = {Josu Arteche and  Jesus Orbe},
	  country	= {},
	  institution	= {},
	  abstract     = {The log periodogram regression is widely used in empirical
applications because of its simplicity, since only a least squares
regression is required 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 and block bootstrap that might
adequately replicate the structure that may arise in the errors of the
regression are 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. Finally,
the performance of the bootstrap in log periodogram regression based
confidence intervals is assessed in different type of models and how
its performance changes as sample size increases.},
	    journal   = {International Journal of Economics and Management Engineering},
	  volume    = {2},
	  number    = {8},
	  year      = {2008},
	  pages     = {823 - 831},
	  ee        = {https://publications.waset.org/pdf/14766},
	  url   	= {https://publications.waset.org/vol/20},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 20, 2008},