Standardized Time Series Lp-Norm Variance Estimators for Simulations

Gamze Tokol, David Goldsman, Daniel H. Ockerman and James J. Swain
Additional contact information
Gamze Tokol: Earley Corporation, Decatur, Georgia 30030
David Goldsman: School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0205
Daniel H. Ockerman: Retek Information Systems, 7 Piedmont Center, Suite 501, Atlanta, Georgia 30305
James J. Swain: Department of Industrial and Systems Engineering, University of Alabama in Huntsville, Huntsville, Alabama 35899

Management Science, 1998, vol. 44, issue 2, 234-245

Abstract: This paper studies a class of estimators for the variance parameter of a stationary stochastic process. The estimators are based on L p norms of standardized time series, and they generalize previously studied estimators due to Schruben. We show that the new estimators have some desirable properties: they are asymptotically unbiased and have low asymptotic variance. We also illustrate empirically the performance of the L p -norm estimators on various stochastic processes.

Keywords: Simulation; Stationary Process; Variance Estimation; Standardized Time Series; Lp-Norm Estimator (search for similar items in EconPapers)
Date: 1998
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:44:y:1998:i:2:p:234-245

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