Time Series Models in Non‐Normal Situations: Symmetric Innovations

@inproceedings{Tiku2000TimeSM,
  title={Time Series Models in Non‐Normal Situations: Symmetric Innovations},
  author={Moti L. Tiku and Wing-Keung Wong and D Christopher Vaughan and Guorui Bian},
  year={2000}
}
We consider AR(q) models in time series with non‐normal innovations represented by a member of a wide family of symmetric distributions (Student's t). Since the ML (maximum likelihood) estimators are intractable, we derive the MML (modified maximum likelihood) estimators of the parameters and show that they are remarkably efficient. We use these estimators for hypothesis testing, and show that the resulting tests are robust and powerful. 

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