Long-range dependent curve time series

Degui Li, Peter M. Robinson*, Han Lin Shang

*Corresponding author for this work

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

We introduce methods and theory for functional or curve time series with long-range dependence. The temporal sum of the curve process is shown to be asymptotically normally distributed, the conditions for this covering a functional version of fractionally integrated autoregressive moving averages. We also construct an estimate of the long-run covariance function, which we use, via functional principal component analysis, in estimating the orthonormal functions spanning the dominant subspace of the curves. In a semiparametric context, we propose an estimate of the memory parameter and establish its consistency. A Monte Carlo study of finite-sample performance is included, along with two empirical applications. The first of these finds a degree of stability and persistence in intraday stock returns. The second finds similarity in the extent of long memory in incremental age-specific fertility rates across some developed nations.
Original languageEnglish
Pages (from-to)957-971
Number of pages15
JournalJournal of the American Statistical Association
Volume115
Issue number530
Early online date30 May 2019
DOIs
Publication statusPublished - 2 Apr 2020
Externally publishedYes

Keywords

  • Curve process
  • Functional FARIMA
  • Functional principal component analysis
  • Limit theorems
  • Long-range dependence

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