On martingale approximations

Abstract

Consider additive functionals of a Markov chain $W_k$, with stationary (marginal) distribution and transition function denoted by $\pi$ and $Q$, say $S_n=g(W_1)+...+g(W_n)$, where $g$ is square integrable and has mean 0 with respect to $\pi$. If $S_n$ has the form $S_n=M_n+R_n$, where $M_n$ is a square integrable martingale with stationary increments and $E(R_n^2)=o(n)$, then $g$ is said to admit a martingale approximation. Necessary and sufficient conditions for such an approximation are developed. Two obvious necessary conditions are $E[E(S_n|W_1{)}^2]=o(n)$ and ${\lim }_{n\to \infty }E(S_n^2)/n<\infty$. Assuming the first of these, let $\Vert g{\Vert }_{+}^2={\mathrm{limsup}}_{n\to \infty }E(S_n^2)/n$; then $\Vert \cdot {\Vert }_{+}$ defines a pseudo norm on the subspace of $L^2(\pi )$ where it is finite. In one main result, a simple necessary and sufficient condition for a martingale approximation is developed in terms of $\Vert \cdot {\Vert }_{+}$. Let $Q^{\ast }$ denote the adjoint operator to $Q$, regarded as a linear operator from $L^2(\pi )$ into itself, and consider co-isometries ($Q{Q}^{\ast }=I$), an important special case that includes shift processes. In another main result a convenient orthonormal basis for $L_0^2(\pi )$ is identified along with a simple necessary and sufficient condition for the existence of a martingale approximation in terms of the coefficients of the expansion of $g$ with respect to this basis.

Published in at http://dx.doi.org/10.1214/07-AAP505 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (4)

• Martingale approximations for sums of stationary processes
• Law of the iterated logarithm for stationary processes
• Comparison between criteria leading to the weak invariance principle
• Exactness of martingale approximation and the central limit theorem

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• Basic properties of the Multivariate Fractional Brownian Motion
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• Pointwise ergodic theorems with rate and application to limit theorems for stationary processes
• Central limit theorem started at a point for additive functionals of reversible Markov chains
• Conditional central limit theorem via martingale approximation
• A New Condition for the Invariance Principle for Stationary Random Fields
• Martingale approximations for random fields
• On the functional CLT for stationary Markov Chains started at a point