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A new CLT for additive functionals of Markov chains

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  • Peligrad, Magda

Abstract

In this paper we study the central limit theorem for additive functionals of stationary Markov chains with general state space by using a new idea involving conditioning with respect to both the past and future of the chain. Practically, we show that any additive functionals of a stationary and totally ergodic Markov chain with var(Sn)∕n uniformly bounded, satisfies a n−central limit theorem with a random centering. We do not assume that the Markov chain is irreducible and aperiodic. However, the random centering is not needed if the Markov chain satisfies stronger forms of ergodicity. In absence of ergodicity the convergence in distribution still holds, but the limiting distribution might not be normal.

Suggested Citation

  • Peligrad, Magda, 2020. "A new CLT for additive functionals of Markov chains," Stochastic Processes and their Applications, Elsevier, vol. 130(9), pages 5695-5708.
    • Handle: RePEc:eee:spapps:v:130:y:2020:i:9:p:5695-5708
    DOI: 10.1016/j.spa.2020.04.004
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    References listed on IDEAS

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    1. Dehling, Herold & Durieu, Olivier & Volny, Dalibor, 2009. "New techniques for empirical processes of dependent data," Stochastic Processes and their Applications, Elsevier, vol. 119(10), pages 3699-3718, October.
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    3. Volný, Dalibor, 1993. "Approximating martingales and the central limit theorem for strictly stationary processes," Stochastic Processes and their Applications, Elsevier, vol. 44(1), pages 41-74, January.
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