Open AccessBootstrap Estimating the Long Memory Parameter of Long Memory Time Series
Author(s) -
Yuhong Xing
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1673/1/012033
Subject(s) - long memory , sieve (category theory) , series (stratigraphy) , mathematics , algorithm , computer science , block (permutation group theory) , statistics , econometrics , discrete mathematics , combinatorics , volatility (finance) , paleontology , biology
This paper evaluates the finite sample performances of three bootstrap methods, sieve AR bootstrap (SARB), fractional differencing sieve bootstrap (FDSB) and fractional differencing block bootstrap (FDBB), inestimating the long memory parameter in long memory time series. Extensive simulations show that the FDSB method outperforms others when estimating long memory parameter, and has stable estimated results in most cases.