Title page for etd-0628111-172154


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URN etd-0628111-172154
Author Hai-Tang Chiou
Author's Email Address No Public.
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Department Applied Mathematics
Year 2010
Semester 2
Degree Master
Type of Document
Language English
Title On the estimation of time series regression coefficients with long range dependence
Date of Defense 2011-05-30
Page Count 86
Keyword
  • Parameter estimation
  • Multiple linear time series regression
  • Variance reduction
  • Long memory process
  • Gauss-Markov bound
  • Abstract In this paper, we study the parameter estimation of the multiple linear time series
    regression model with long memory stochastic regressors and innovations. Robinson and
    Hidalgo (1997) and Hidalgo and Robinson (2002) proposed a class of frequency-domain
    weighted least squares estimates. Their estimates are shown to achieve the Gauss-Markov
    bound with standard convergence rate. In this study, we proposed a time-domain generalized LSE approach, in which the inverse autocovariance matrix of the innovations is estimated via autoregressive coefficients. Simulation studies are performed to compare the proposed estimates with Robinson and Hidalgo (1997) and Hidalgo and Robinson (2002). The results show the time-domain generalized LSE is comparable to Robinson and Hidalgo (1997) and Hidalgo and Robinson (2002) and attains higher efficiencies when the
    autoregressive or moving average coefficients of the FARIMA models have larger values.
    A variance reduction estimator, called TF estimator, based on linear combination of the
    proposed estimator and Hidalgo and Robinson (2002)'s estimator is further proposed to
    improve the efficiency. Bootstrap method is applied to estimate the weights of the linear combination. Simulation results show the TF estimator outperforms the frequency-domain as well as the time-domain approaches.
    Advisory Committee
  • Mong-Na Lo Huang - chair
  • May-Ru Chen - co-chair
  • Shih-Feng Huang - co-chair
  • Mei-Hui Guo - advisor
  • Ching-Kang Ing - advisor
  • Files
  • etd-0628111-172154.pdf
  • indicate accessible in a year
    Date of Submission 2011-06-28

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