Confidence Interval Estimation for the Variance Parameter of Stationary Processes

Bor-Chung Chen and Robert G. Sargent
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Bor-Chung Chen: Department of Industrial Engineering and Operations Research, Syracuse University, Syracuse, New York 13244
Robert G. Sargent: Department of Industrial Engineering and Operations Research, Syracuse University, Syracuse, New York 13244

Management Science, 1990, vol. 36, issue 2, 200-211

Abstract: Asymptotic confidence interval estimators of the variance parameter \sigma 2 = lim n -> \infty n Var((1/n) \sum n i = 1 X i ) are described in this paper for observations X 1 , X 2 ,...,X n from a strictly stationary phi-mixing stochastic process. They are based on asymptotic properties of the standardized time series of observations from the process. The new point and interval estimators for the variance parameter are compared to the classical batch means estimator. The results show that the new estimators have asymptotic properties that clearly dominate the classical estimator. Also, asymptotic confidence interval estimators for the ratio of two variance parameters representing two independent processes are discussed.

Keywords: simulation; stationary time series; confidence intervals; variance estimation (search for similar items in EconPapers)
Date: 1990
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:36:y:1990:i:2:p:200-211

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