Testing for changes in autocovariances of nonparametric time series models

Xiaoye Li, Zhibiao Zhao

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

In the literature on change-point analysis, much attention has been paid to detecting changes in certain marginal characteristics, such as mean, variance, and marginal distribution. For time series data with nonparametric time trend, we study the change-point problem for the autocovariance structure of the unobservable error process. To derive the asymptotic distribution of the cumulative sum test statistic, we develop substantial theory for uniform convergence of weighted partial sums and weighted quadratic forms. Our asymptotic results improve upon existing works in several important aspects. The performance of the test statistic is examined through simulations and an application to interest rates data.

Original languageEnglish (US)
Pages (from-to)237-250
Number of pages14
JournalJournal of Statistical Planning and Inference
Volume143
Issue number2
DOIs
StatePublished - Feb 1 2013

Fingerprint

Autocovariance
Nonparametric Model
Time Series Models
Test Statistic
Time series
Change-point Analysis
Statistics
Change-point Problem
Testing
Cumulative Sum
Interest Rates
Partial Sums
Uniform convergence
Marginal Distribution
Time Series Data
Weighted Sums
Quadratic form
Asymptotic distribution
Simulation
Change point

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

Cite this

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Testing for changes in autocovariances of nonparametric time series models. / Li, Xiaoye; Zhao, Zhibiao.

In: Journal of Statistical Planning and Inference, Vol. 143, No. 2, 01.02.2013, p. 237-250.

Research output: Contribution to journalArticle

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