Testing Time Series Linearity. Traditional and Bootstrap Methods

Arthur Berg, Timothy McMurry, Dimitris N. Politis

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Scopus citations

Abstract

We review the notion of time series linearity and describe recent advances in linearity and Gaussianity testing via data resampling methodologies. Many advances have been made since the first published tests of linearity and Gaussianity by Subba Rao and Gabr in 1980, including several resampling-based proposals. This chapter is intended to be instructive in explaining and motivating linearity testing. Recent results on the validity of the AR-sieve bootstrap for linearity testing are reviewed. In addition, a subsampling-based linearity and Gaussianity test is proposed where asymptotic consistency of the testing procedure is justified.

Original languageEnglish (US)
Title of host publicationHandbook of Statistics
PublisherElsevier B.V.
Pages27-42
Number of pages16
DOIs
StatePublished - 2012

Publication series

NameHandbook of Statistics
Volume30
ISSN (Print)0169-7161

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modeling and Simulation
  • Applied Mathematics

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    Berg, A., McMurry, T., & Politis, D. N. (2012). Testing Time Series Linearity. Traditional and Bootstrap Methods. In Handbook of Statistics (pp. 27-42). (Handbook of Statistics; Vol. 30). Elsevier B.V.. https://doi.org/10.1016/B978-0-444-53858-1.00002-8