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Limit Theory for M-Estimates in an Integrated Infinite Variance

Published online by Cambridge University Press:  11 February 2009

Keith Knight
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Abstract

We consider the limiting distributions of M-estimates of an “autoregressive” parameter when the observations come from an integrated linear process with infinite variance innovations. It is shown that M-estimates are, asymptotically, infinitely more efficient than the least-squares estimator (in the sense that they have a faster rate of convergence) and are conditionally asymptotically normal.

Type
Research Article
Information
Econometric Theory , Volume 7 , Issue 2 , June 1991 , pp. 200 - 212
Copyright
Copyright © Cambridge University Press 1991

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References

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