AR Models with Stationary Non-Gaussian Real-Valued Marginals

N. Balakrishna ()
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N. Balakrishna: Cochin University of Science and Technology, Department of Statistics

Chapter Chapter 4 in Non-Gaussian Autoregressive-Type Time Series, 2021, pp 93-126 from Springer

Abstract: Abstract If the time series models with Gaussian marginal distributions fail to describe a real situation, it is natural to try a model generating sequence of real-valued rvs following some skewed or heavy-tailed distributions. In this chapter, we study the properties of such models. Probabilistic properties of autoregressive processes with stationary marginal distributions such as Laplace, generalized Laplace, Gumbel’s extreme value, logistic, hyperbolic, Cauchy etc. are discussed in this chapter. In these cases explicit forms of the innovation distributions are available for specified marginal distributions. This chapter also discusses autoregressive models with stable marginals. A suitable method of estimation is proposed to estimate the parameters in each of the models.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-16-8162-2_4

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DOI: 10.1007/978-981-16-8162-2_4

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