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Linear Time Series Models with Non-Gaussian Innovations

In: Non-Gaussian Autoregressive-Type Time Series

Author

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  • N. Balakrishna

    (Cochin University of Science and Technology, Department of Statistics)

Abstract

The time series models with normally distributed innovations generate stationary normal sequences. However, if the innovations are not normal then the stationary marginal distribution may be a member of an entirely different family. This chapter discusses autoregressive models with innovations belonging to various classes of non-Gaussian distributions. Detailed analysis of the models with innovation distributions such as stable, Laplace, heavy-tailed, exponential, gamma, and mixed normal is considered along with the problem of estimation.

Suggested Citation

  • N. Balakrishna, 2021. "Linear Time Series Models with Non-Gaussian Innovations," Springer Books, in: Non-Gaussian Autoregressive-Type Time Series, chapter 0, pages 155-194, Springer.
    • Handle: RePEc:spr:sprchp:978-981-16-8162-2_6
    DOI: 10.1007/978-981-16-8162-2_6
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