AR Models with Stationary Non-Gaussian Positive Marginals

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

Chapter Chapter 3 in Non-Gaussian Autoregressive-Type Time Series, 2021, pp 41-92 from Springer

Abstract: 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.

Date: 2021
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-16-8162-2_3

Ordering information: This item can be ordered from
http://www.springer.com/9789811681622

DOI: 10.1007/978-981-16-8162-2_3

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().