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Semiparametric methods in nonlinear time series analysis: a selective review

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  • Patrick Saart
  • Jiti Gao
  • Nam Hyun Kim

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.

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  • Patrick Saart & Jiti Gao & Nam Hyun Kim, 2014. "Semiparametric methods in nonlinear time series analysis: a selective review," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(1), pages 141-169, March.
    • Handle: RePEc:taf:gnstxx:v:26:y:2014:i:1:p:141-169
    DOI: 10.1080/10485252.2013.840724
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