Semiparametric methods in nonlinear time series analysis: a selective review

Saart, Patrick A. ORCID: https://orcid.org/0000-0002-7611-0383, Gao, Jiti and Kim, Nam Hyun 2014. Semiparametric methods in nonlinear time series analysis: a selective review. Journal of Nonparametric Statistics 26 (1) , pp. 141-169. 10.1080/10485252.2013.840724

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Abstract

Time series analysis is a tremendous research area in statistics and econometrics. In a previous review, the author was able to break down up 15 key areas of research interest in time series analysis. Nonetheless, the aim of the review in this current paper is not to cover a wide range of somewhat unrelated topics on the subject, but the key strategy of the review in this paper is to begin with a core the ‘curse of dimensionality’ in nonparametric time series analysis, and explore further in a metaphorical domino-effect fashion into other closely related areas in semiparametric methods in nonlinear time series analysis.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Business (Including Economics)
Publisher:	Taylor & Francis
ISSN:	1048-5252
Date of First Compliant Deposit:	27 November 2018
Date of Acceptance:	1 July 2012
Last Modified:	06 Jan 2024 03:09
URI:	https://orca.cardiff.ac.uk/id/eprint/117139

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