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

2005-04-01
Akkaya, AD
Tiku, ML
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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.
Statistics, Probability and Uncertainty, Statistics and Probability
https://hdl.handle.net/11511/64932
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.
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