Martingale decomposition and approximations for nonlinearly dependent processes

Research output: Contribution to journalArticlepeer-review

Abstract

This paper proposes a new martingale (MG) decomposition (Gordin, 1969; Hall and Heyde, 1980) for a dependent time series under a predictive dependence measure based on Wu (2005). The decomposition produces a generalized version of the Beveridge–Nelson (BN) lemma (Phillips and Solo, 1992) that accommodates many nonlinear time series, such as GARCH models and threshold autoregressive processes, thereby extending the empirical ambit of the original lemma designed for the linear process. Under this extended framework, MG approximations can be constructed for weighted sums of the nonlinear dependent processes and these approximations lead directly to a new central limit theorem whose range of application includes many practical time series models.

Original languageEnglish (US)
Pages (from-to)35-42
Number of pages8
JournalStatistics and Probability Letters
Volume152
DOIs
StatePublished - Sep 2019

Keywords

  • Dependence
  • Martingale approximations
  • Nonlinear time series

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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