The Annals of Statistics

Local stationarity and time-inhomogeneous Markov chains

Lionel Truquet

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Abstract

A primary motivation of this contribution is to define new locally stationary Markov models for categorical or integer-valued data. For this initial purpose, we propose a new general approach for dealing with time-inhomogeneity that extends the local stationarity notion developed in the time series literature. We also introduce a probabilistic framework which is very flexible and allows us to consider a much larger class of Markov chain models on arbitrary state spaces, including most of the locally stationary autoregressive processes studied in the literature. We consider triangular arrays of time-inhomogeneous Markov chains, defined by some families of contracting and slowly-varying Markov kernels. The finite-dimensional distribution of such Markov chains can be approximated locally with the distribution of ergodic Markov chains and some mixing properties are also available for these triangular arrays. As a consequence of our results, some classical geometrically ergodic homogeneous Markov chain models have a locally stationary version, which lays the theoretical foundations for new statistical modeling. Statistical inference of finite-state Markov chains can be based on kernel smoothing and we provide a complete and fast implementation of such models, directly usable by the practitioners. We also illustrate the theory on a real data set. A central limit theorem for Markov chains on more general state spaces is also provided and illustrated with the statistical inference in INAR models, Poisson ARCH models and binary time series models. Additional examples such as locally stationary regime-switching or SETAR models are also discussed.

Article information

Source
Ann. Statist., Volume 47, Number 4 (2019), 2023-2050.

Dates
Received: October 2017
Revised: June 2018
First available in Project Euclid: 21 May 2019

Permanent link to this document
https://projecteuclid.org/euclid.aos/1558425638

Digital Object Identifier
doi:10.1214/18-AOS1739

Mathematical Reviews number (MathSciNet)
MR3953443

Zentralblatt MATH identifier
07082278

Subjects
Primary: 62M05: Markov processes: estimation 62G08: Nonparametric regression
Secondary: 62M10: Time series, auto-correlation, regression, etc. [See also 91B84]

Keywords
Markov chains local stationarity

Citation

Truquet, Lionel. Local stationarity and time-inhomogeneous Markov chains. Ann. Statist. 47 (2019), no. 4, 2023--2050. doi:10.1214/18-AOS1739. https://projecteuclid.org/euclid.aos/1558425638


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Supplemental materials

  • Supplement to “Local stationarity and time-inhomogeneous Markov chains.”. Contains the proofs of all the results as well as a discussion of various Markov chains models satisfying the assumptions used in this paper.