Inference for Nonlinear Time Series Models

Kamil Feridun Turkman, Manuel González Scotto and Patrícia de Zea Bermudez
Additional contact information
Kamil Feridun Turkman: Faculdade de Ciências Universidade de Lisboa, Departmento de Estatística e Investigação Operacional
Manuel González Scotto: Universidade de Aveiro, Departamento de Matemática
Patrícia de Zea Bermudez: Faculdade de Ciências Universidade de Lisboa, Departmento de Estatística e Investigação Operacional

Chapter Chapter 4 in Non-Linear Time Series, 2014, pp 121-197 from Springer

Abstract: Abstract Suppose we have an observed time series $$x_{1},x_{2},\ldots,x_{n}$$ and want to know if a linear time series model is adequate for the data, or an alternative nonlinear model should be considered. Linear models are often taken as the null hypotheses against a nonlinear alternative due to the simplicity of inference. Often we know much about the underlying process which generate the data set. Therefore it is possible to decide if a linear model will be adequate and if not, what aspects of nonlinearity should be modeled as alternative.

Keywords: Markov Chain Monte Carlo; Posterior Density; Markov Chain Monte Carlo Method; Generalize Pareto Distribution; Proposal Distribution (search for similar items in EconPapers)
Date: 2014
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:978-3-319-07028-5_4

Ordering information: This item can be ordered from
http://www.springer.com/9783319070285

DOI: 10.1007/978-3-319-07028-5_4

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 ().