Let $(X_n)_n$ be a sequence of i.i.d. random variables, and let $\rho \in (0,1)$. Is there any asymptotic Theorem for the following random variable:

$$ Y = \lim_{n\rightarrow \infty}\sum_{i=0}^n \rho^iX_i $$ in the fashion of a Law of large numbers? My conjecture is that $Y \rightarrow^{a.s.} \frac{\mathbb{E}[X]}{1-\rho}$, but I am not able to find a proof nor a reference about it.

  • 2
    $\begingroup$ The conjecture can't be correct - for example, suppose the $X_i$ are Bernoulli distributed with parameter 0.5 and $\rho$ is 0.1. Then if $X_1 = 0$, it is impossible for $Y$ to ever exceed 0.5, so it can't almost surely be equal to something greater than 0.5. $\endgroup$
    – fblundun
    Commented Mar 16, 2021 at 11:25
  • $\begingroup$ That wonderful counterexample by @fblundun exposes the basic problem: as $n$ increases, the contributions of the first few $X_i$ will persist: the only possible way we could hope for a limiting theorem would be for the contributions of the remaining $X_i$ eventually to overwhelm the first few. Since that's impossible when $|\rho|\lt 1,$ pursuing any result of this type looks hopeless. $\endgroup$
    – whuber
    Commented Mar 16, 2021 at 15:34

1 Answer 1


To see why this is not possible, let $\psi_Y$ be the cumulant generating function for $Y$. It is defined for any real number $t$ by

$$\psi_Y(t) = \log E\left[e^{itY}\right].$$

If the sum in the question converges to a random variable $Y,$ then taking $X$ to be distributed like all the $X_i$ and independent of them we see

$$Y \sim \lim_{n\to\infty} X + \rho X_0 + \rho^2 X_1 + \cdots = X + \lim_{n\to\infty} \rho\left(X_0 + \rho X_1 + \cdots\right) = X + \rho Y .$$

This entails

$$\psi_Y(t)=\psi_X(t) + \psi_Y( \rho t ) $$

for all $t.$ We may recover $\psi_X$ from the distribution $Y$ via

$$\psi_X(t) = \psi_Y(t) - \psi_Y(\rho t)$$

and, since the c.g.f $\psi_X$ determines the distribution of $X,$ we can reverse the process: $Y$ determines the common distribution of the $X_i.$

Consequently there can be no analog of the laws of large numbers, which posit convergence to universal limiting distributions (under mild assumptions about the $X_i$), effectively wiping out almost all information about the underlying distribution used to arrive at the limit.


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