The performance of bootstrapping autoregressive AR (9) process on the Malaysian opening price for second board

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1 Citation (Scopus)

Abstract

The commonly used Maximum Likelihood Estimator (MLE) to estimate the parameters of a time series model requires that the process is normally distributed. However, in real situations, many processes are not normal and have a heavy tail distribution. Hence, the aim of this study is to propose using a distribution free bootstrap method for parameter estimations, when the assumption of normality is not met. The performance of the Bootstrap Estimates (BE) and the MLE estimates of the AR (9) process were then investigated using the Malaysian Opening Price for Second Board data and simulation study. The empirical results indicate that the BE is reasonably close to the MLE estimates, hence, can be established as one reliable alternative approach to the MLE estimates.

Original languageEnglish
Pages (from-to)2101-2107
Number of pages7
JournalJournal of Applied Sciences
Volume10
Issue number18
Publication statusPublished - 2010

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Bootstrapping
Maximum likelihood estimator
Bootstrap
Time series models
Parameter estimation
Heavy tails
Empirical results
Normality
Bootstrap method
Simulation study
Distribution-free

Keywords

  • AR process
  • Bootstrap estimates
  • Maximum likelihood estimator
  • Residual bootstrap
  • Root mean squared errors

ASJC Scopus subject areas

  • General

Cite this

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abstract = "The commonly used Maximum Likelihood Estimator (MLE) to estimate the parameters of a time series model requires that the process is normally distributed. However, in real situations, many processes are not normal and have a heavy tail distribution. Hence, the aim of this study is to propose using a distribution free bootstrap method for parameter estimations, when the assumption of normality is not met. The performance of the Bootstrap Estimates (BE) and the MLE estimates of the AR (9) process were then investigated using the Malaysian Opening Price for Second Board data and simulation study. The empirical results indicate that the BE is reasonably close to the MLE estimates, hence, can be established as one reliable alternative approach to the MLE estimates.",
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