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A new autoregressive time series model in exponential variables (NEAR(1))

Published online by Cambridge University Press:  01 July 2016

A. J. Lawrance  and
P. A. W. Lewis
A. J. Lawrance*
Affiliation:
University of Birmingham
P. A. W. Lewis*
Affiliation:
Naval Postgraduate School, Monterey
*
∗Postal address: Department of Statistics, The University of Birmingham, P.O. Box 363, Birmingham B15 2TT, U.K.
∗∗Postal address: Department of Operations Research, Naval Postgraduate School, Monterey, CA 93940, U.S.A.
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Abstract

A new time series model for exponential variables having first-order autoregressive structure is presented. Unlike the recently studied standard autoregressive model in exponential variables (ear(1)), runs of constantly scaled values are avoidable, and the two parameter structure allows some adjustment of directional effects in sample path behaviour. The model is further developed by the use of cross-coupling and antithetic ideas to allow negative dependency. Joint distributions and autocorrelations are investigated. A transformed version of the model has a uniform marginal distribution and its correlation and regression structures are also obtained. Estimation aspects of the models are briefly considered.

Keywords

AUTOREGRESSIVE MODEL IN EXPONENTIAL VARIABLESNEGATIVE CORRELATIONCROSS-COUPLED PROCESSESANTITHETIC VARIABLESCORRELATED UNIFORM PROCESSTIME SERIESPOINT PROCESSSIMULATION
Type
Research Article
Information
Advances in Applied Probability , Volume 13 , Issue 4 , December 1981 , pp. 826 - 845
Copyright
Copyright © Applied Probability Trust 1981 

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Footnotes

Research supported by the U.K. Science and Engineering Research Council (Grant A185193), the U.S. Office of Naval Research (Grant NR-42-284) and the Naval Postgraduate School Foundation.

References

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