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AR Models with Stationary Non-Gaussian Positive Marginals

In: Non-Gaussian Autoregressive-Type Time Series

Author

Listed:
  • N. Balakrishna

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

Abstract

The Markov sequences of non-negative random variables play important role in modelling the time to events and time series. This chapter provides a detailed analysis of models useful for defining stationary time series of non-negative random variables. An important problem here is to determine the explicit form of the innovation distribution for a specified stationary marginal distribution. Autoregressive models with constant and random coefficients are introduced to define sequences of random variables with standard marginal distributions such as exponential, gamma, beta, inverse Gaussian. Suitable methods of estimation are proposed for all models and the properties of the resulting estimators are studied.

Suggested Citation

  • N. Balakrishna, 2021. "AR Models with Stationary Non-Gaussian Positive Marginals," Springer Books, in: Non-Gaussian Autoregressive-Type Time Series, chapter 0, pages 41-92, Springer.
    • Handle: RePEc:spr:sprchp:978-981-16-8162-2_3
    DOI: 10.1007/978-981-16-8162-2_3
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