Section 9.5

The ergodic theorem

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Problem 9.5.1

Let T={1,2,…}T = \left\{ 1,2, \ldots \right\} and let ℓ\ell be the set of invariant events of (RT,BT)\left(\mathbb {R}^{T}, \mathscr {B}^{T}\right). Show that ℓ\ell is a σ\sigma-field.

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Problem 9.5.2

Assume that X1,X2,…X_{1}, X_{2}, \ldots is a stationary sequence with autocovariance function c(m)c(m). Show that

Var⁡[1n∑i=1nXi]=2n2∑j=1n∑i=0j−1c(i)−c(0)n \operatorname {Var}\left[\frac{1}{n} \sum _{i = 1}^{n} X_{i}\right] = \frac{2}{n^{2}} \sum _{j = 1}^{n} \sum _{i = 0}^{j-1} c(i)-\frac{c(0)}{n}

Assuming that j−1∑i=0j−1c(i)→σ2j^{-1} \sum_{i = 0}^{j-1} c(i) \rightarrow \sigma^{2} as j→∞j \rightarrow \infty, show that

Var⁡[1n∑i=1nXi]→σ2 as n→∞ \operatorname {Var}\left[\frac{1}{n} \sum _{i = 1}^{n} X_{i}\right] \rightarrow \sigma ^{2} \quad \text{ as } n \rightarrow \infty
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Problem 9.5.3

Let X1,X2,…X_{1}, X_{2}, \ldots be independent identically distributed random variables with zero mean and unit variance. Let

Yn=∑i=0∞αiXn+i for n≥1 Y_{n} = \sum _{i = 0}^{\infty } \alpha _{i} X_{n+i} \quad \text{ for } n \geq 1

where the αi\alpha_{i} are constants satisfying ∑iαi2<∞\sum_{i} \alpha_{i}^{2} < \infty. Use the martingale convergence theorem to show that the above summation converges almost surely and in mean square. Prove that n−1∑i=1nYi→0n^{-1} \sum_{i = 1}^{n} Y_{i} \rightarrow 0 a.s. and in mean, as n→∞n \rightarrow \infty.

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