Time series AR(1) model for short-tailed distributions

Date

2005-04-01

Author

Akkaya, AD
Tiku, ML

Metadata

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The innovations in AR(1) models in time series have primarily been assumed to have a normal or long-tailed distributions. We consider short-tailed distributions (kurtosis less than 3) and derive modified maximum likelihood (MML) estimators. We show that the MML estimator of 0 is considerably more efficient than the commonly used least squares estimator and is also robust. This paper is essentially the first to achieve robustness to inliers and to various forms of short-tailedness in time series analysis.

Subject Keywords

Statistics, Probability and Uncertainty, Statistics and Probability

URI

https://hdl.handle.net/11511/64932

Journal

STATISTICS

DOI

https://doi.org/10.1080/02331880500031407

Collections

Department of Statistics, Article

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Citation Formats

A. Akkaya and M. Tiku, “Time series AR(1) model for short-tailed distributions,” STATISTICS, pp. 117–132, 2005, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/64932.