Taking logarithms of the product givesBy part (d)(i), $V_j=E_j-\log b$ in probability distribution, where $E_j$ has the exponential distribution of rate one. Thus $\mathbb{E}|V_j|\le 1+|\log b|<\infty$ and $\mathbb{E}{V}_j=1-\log b$. The strong law of large numbers givesIf $b<e$, choose $\delta =(1-\log b)/2>0$. Eventually $\log W_n\le -\delta n$, so $W_n\le e^{-\delta n}\to 0$. If $b>e$, choose $\delta =(\log b-1)/2>0$; eventually $W_n\ge e^{\delta n}\to \infty$. ThereforeIn particular, the second range cannot have almost sure convergence to zero. This is the almost-sure growth rate of independent products. At $b=e$ the logarithmic limit is zero, which by itself decides neither behavior; that boundary case is not requested.

The inverse transformation of a random variable is $u=e^{-v}$, with absolute derivative $e^{-v}$. The condition $0<u<b$ becomes $v>-\log b$. The change of variables for probability densities givesEquivalently, $V_n+\log b$ has the exponential distribution of rate one. Applying a separate measurable transformation of a random variable to each independent $U_n$ preserves independence of random variables, so the $V_n$ are independent and identically distributed.

For $\alpha >0$, integration and independence of random variables giveBy the fractional moment criterion for convergence in probability,The parenthesized factor is less than one precisely when $\alpha \log b-\log (1+\alpha )<0$. This expression vanishes at $\alpha =0$ and has derivative $\log b-1$ there. If $b<e$, the derivative is negative, so some sufficiently small $\alpha >0$ makes the factor strictly less than one. The bound then tends to zero geometrically, proving

If $0<b\le 1$, then $0<U_j\le 1$ almost surely. Intersecting these events over all $j$ still gives an event of probability one. On it, $0\le W_{n+1}\le W_n$, so the products have a finite nonnegative limit $W_{\infty }$.

For every $\epsilon >0$, the inclusion $\{W_{\infty }>\epsilon \}\subseteq \{W_n>\epsilon \}$ and part (c)(i) imply $\mathbb{P}(W_{\infty }>\epsilon )=0$. Taking $\epsilon =1/m$ for positive integers $m$ proves $W_{\infty }=0$ almost surely. ThusThe monotonicity is the extra ingredient that upgrades convergence in probability to almost sure convergence here.

Direct integration of the uniform distribution yields $\mathbb{E}{U}_j=b/2$ and $\mathbb{E}{U}_j^2=b^2/3$. The expectation of a product of independent random variables givesSubtracting the square of the expected value gives the variance:Since $W_n\ge 0$, Markov's inequality implies, for every $\epsilon >0$,Hence $\boxed{W_n\to 0\text{ in probability for }0<b<2.}$ The first moment suffices: the variance need not tend to zero throughout this range.

The restriction $Q_C$ is a finite Markov chain that is an irreducible Markov chain. It is aperiodic, because its upper states have self-transition probability $a/(a+b)>0$, and the period is constant on a communicating class. We now find its stationary distribution.

Every column of $Q_C$ sums to one. Here is the full incoming-mass check, with each total measured in units of $1/(a+b)$. For $j\in L$, the incoming contributions are $a$ from $j+b\in I$ and $b$ from the self-transition. For $j\in U$, they are $b$ from $j-a\in I$ and $a$ from the self-transition.

For $j\in I$, the upward predecessor $j-a$ lies in $I$ exactly when $j\ge -c+a$, contributing $b$ units. When $j\le -c+a-1$, that predecessor is replaced by resets from all $b$ states of $L$, again contributing $b$ units. Independently, the downward predecessor $j+b$ contributes $a$ units when $j\le c-b$; when $j\ge c-b+1$, resets from the $a$ states of $U$ contribute those $a$ units instead. Thus every column total is $a+b$ units.

Therefore $Q_C$ is a doubly stochastic matrix, and the uniform distribution on $C$ is stationary:The countable-state Markov chain convergence theorem, applied to the finite closed class $C$, gives $Q_C^n(i,j)\to {\pi }_j$ for every $i\in C$. Its hypotheses hold: $C$ is finite and irreducible, hence positive recurrent, and it is aperiodic.

If $i_0>c+a$, the hitting time $H$ of $C$ has $\mathbb{P}(H>n)=(a/(a+b){)}^n$. If $i_0<-c-b$, the corresponding tail is $(b/(a+b){)}^n$. Thus $H<\infty$ almost surely from every exterior initial state. Conditioning on $H$ and $R_H$, use convergence within $C$ after entrance and the bounded convergence theorem; equivalently, first restrict to $H\le K$ and then let $K\to \infty$. This proves, for every $i_0\in \mathbb{Z}$,The limiting distribution is independent of $i_0$.

DefineAn interior jump cannot go below $-c-b$ or above $c+a$, and every exterior reset lands in $I$. Thus $C$ is closed.

To prove that it is one communicating class, start anywhere in $C$. From $L$ or $U$, a reset can reach $I$ with positive probability. From any state of $I$, repeated $+a$ steps reach some state of $U$, after staying in $I$ up to the last step. A reset from this state can reach every point of the consecutive blockEvery integer $j\in \{-c-b,\dots ,c\}$ has the form $t-kb$ for some $t\in B$ and an integer $k\ge 0$. Repeated $-b$ steps from $t$ reach $j$, with all departure states in $I$ until a possible last step into $L$. This gives access to all of $I\cup L$. Finally, each $j\in U$ can be reached from $j-a\in I$. Every path described has strictly positive probability. Therefore every state in $C$ reaches every other state in $C$.

Each state outside $C$ can only stay put or reset into $I$, and no transition from $C$ returns to it. Different states outside $C$ cannot reach one another. Hence the complete list of communicating classes isThe finite class $C$ is the unique closed communicating class. The exterior singleton classes are transient communicating classes, since their self-transition probabilities are strictly less than one. Notice that this reachability proof does not require $a,b$ to be coprime.

First identify the recurrence type, rather than infer an occupation limit from a one-time probability. At time $k(a+b)$, return to zero requires exactly $kb$ upward steps. The binomial distribution giveswhere the last step is Stirling's approximation. At all other positive times the return probability is zero. Hence ${\sum }_{n\ge 0}{\mathbb{P}}_0(S_n=0)=\infty$. The recurrence criterion by return probabilities proves the recurrent property; by the irreducible Markov chain property established in (i), every state is recurrent. The equivalence of positive recurrence and existence of a stationary distribution for an irreducible Markov chain on a countable state space, together with (iii), makes this chain null recurrent.

For completeness, the zero occupation proportion follows directly by treating returns as a renewal process. Let $L_1,L_2,\dots$ be successive strictly positive return-cycle lengths to zero. The Strong Markov property makes them independent and identically distributed, the recurrent property makes them finite almost surely, and the null recurrent property gives $\mathbb{E}{L}_1=\infty$. For each positive integer $K$, the strong law of large numbers applied to $\min (L_j,K)$ givesTake a common event of probability one for these countably many $K$. By the monotone convergence theorem, the right side increases to infinity. Thus the number of completed return cycles by time $n$, divided by $n$, tends to zero. Counting the initial visit changes this ratio by at most $1/n$. Consequently,The walk visits zero infinitely often, but the asymptotic fraction of time spent there is zero.

Suppose a stationary distribution $\pi$ exists. Its stationarity equations areSince ${\sum }_j{\pi }_j=1$, its masses tend to zero as $|j|\to \infty$, and a positive maximum $M={\pi }_{j_0}$ is attained. Both terms on the right are at most $M$. Since $p,q>0$ and $p+q=1$, equality forces ${\pi }_{j_0-a}={\pi }_{j_0+b}=M$. Applying the same argument repeatedly at $j_0-a,j_0-2a,\dots$ givesThese are infinitely many distinct states, contradicting ${\sum }_j{\pi }_j=1$. ThusThis maximum principle proof uses neither a finite expected value of the state nor the coprimality assumption.

A return path with $u$ upward steps and $v$ downward steps satisfies $au=bv$. Since $a,b$ are coprime, this is equivalent to $u=bk$ and $v=ak$ for an integer $k\ge 1$. Its length is therefore $k(a+b)$.

Conversely, for each such $k$, any ordering of $bk$ upward and $ak$ downward steps is an allowed positive-probability return path. The possible strictly positive return times are exactly the positive multiples of $a+b$. By the definition of the period of a state in a Markov chain as their greatest common divisor,In particular, none of the states is aperiodic.

Fix any two integers $i,j$. By Bézout's identity, there are integers $u,v$ with $au-bv=j-i$. Replacing $(u,v)$ by $(u+kb,v+ka)$ leaves this identity unchanged. Choose $k$ large enough that both entries are nonnegative. A path with $u+kb$ upward steps and $v+ka$ downward steps then takes $i$ to $j$, with strictly positive probability $p^{u+kb}{q}^{v+ka}$. There are no boundary restrictions on this state space.

This applies in both directions, so every pair of states communicates. Hence

It suffices that the moment-generating function be finite on some open interval containing zero:Equivalently, some two-sided exponential moment exists: $\mathbb{E}{e}^{r|Y|}<\infty$ for some $r>0$. For the forward implication, choose $0<r<\delta$ and use $e^{r|Y|}\le e^{rY}+e^{-rY}$; the converse follows from $e^{tY}\le e^{r|Y|}$ for $|t|\le r$.

Under this weaker hypothesis, all moments exist. For $|t|<r$, dominated convergence justifies the Taylor series argument in (i). Vanishing odd moments gives $M_Y(t)=M_{-Y}(t)$ near zero, and the local uniqueness theorem for moment-generating functions gives the symmetric distribution. The reverse implication follows directly from equality of the two probability distributions.

More generally, it suffices that all moments exist and the probability distribution have moment determinacy: $Y$ and $-Y$ then have identical moments and hence identical probability distributions. Existence of all moments by itself does not provide that uniqueness.

Take a discrete probability distribution on three points:The probabilities are positive and sum to one. Direct calculation givesandNevertheless it is not a symmetric distribution, since $\mathbb{P}(Y=3)=1/10$ while $\mathbb{P}(Y=-3)=0$. Vanishing of just the first and third moments does not force all odd moments to vanish.

Let $X$ have the Pareto distribution with probability density function $f_X(x)=3x^{-4}$ for $x\ge 1$, and zero otherwise. Its normalization of a probability distribution and first two moments follow fromSet $Y=X-3/2$. ThenThe probability distribution of $Y$ is not a symmetric distribution: $Y\ge -1/2$ almost surely, whereas $\mathbb{P}(Y>1/2)=\mathbb{P}(X>2)=1/8>0$ and $\mathbb{P}(Y<-1/2)=0$. For $X\ge 3$ we have $|Y|\ge X/2$, soThus $\boxed{Y=X-3/2}$ has every property required.

First justify that the moments being used exist. For every $r>0$,The bound $|y{|}^k\le k!r^{-k}{e}^{r|y|}$ gives $\mathbb{E}|Y{|}^k<\infty$ for all $k$.

If $Y$ has a symmetric distribution, then $Y$ and $-Y$ have the same probability distribution, so for odd $k$,which forces $\mathbb{E}{Y}^k=0$.

Conversely, suppose every odd moment vanishes. For fixed $t\in \mathbb{R}$, the absolute values of the partial Taylor series for $e^{tY}$ are bounded by $e^{|t||Y|}$, whose expected value is finite by the preceding inequality. The dominated convergence theorem therefore permits termwise expectation:The uniqueness theorem for moment-generating functions implies equality of the probability distributions of $Y$ and $-Y$. Hence

The possible limiting correlation coefficients areThe direction of the dependence comes from inversion of the same counting process: extra arrivals produce a larger count but an earlier $n$-th Poisson arrival time. Shared fluctuations therefore enter the two coordinates with opposite signs. Positive limiting correlation cannot arise; this can be formalized using negative quadrant dependence of a Poisson count and an arrival time.

At the same time scale, $f(n)=n$, part (iii) rules out every positive-positive quadrant. A bivariate normal distribution with standard normal marginal distributions and $\rho >-1$ assigns positive probability to such a quadrant. Thus, under the assumed joint limit, this choice forces $\rho =-1$. At very different time scales, $f(n)=n^4$, part (iv) gives $\rho =0$.

Intermediate time scales retain an intermediate fraction of the same arrival fluctuations. In particular, the Poisson count-arrival joint central limit theorem gives $\rho =-1/\sqrt{\lambda }$ for $f(n)=\lambda n$ with $1\le \lambda <\infty$. These choices realize every $\rho \in [-1,0)$, and $f(n)=n^4$ supplies zero. This identifies the whole interval without requiring an additional limit calculation in this part.

The premise about a bivariate normal distribution is understood to apply when a joint limit exists. Merely requiring $f(n)\to \infty$ cannot guarantee one: taking $f(n)=n$ for even $n$ and $f(n)=n^4$ for odd $n$ gives different limits along the two subsequences just identified.

Separate a long future count from the early Poisson arrival time. Let ${\mathcal{F}}_t=\sigma (N_s:0\le s\le t)$ be the natural filtration, and defineThe truncated Poisson arrival time in $B_n$ is ${\mathcal{F}}_{n^2}$-measurable. The independent increments property makes $A_n$ independent of ${\mathcal{F}}_{n^2}$, and hence of $B_n$.

The numerator of $A_n$ is a centered Poisson random variable of parameter $n^4-n^2$. Apply the central limit theorem to the $n^4-n^2$ unit-time increments, then use $\sqrt{n^4-n^2}/n^2\to 1$ and Slutsky's theorem. This proves $A_n\stackrel{\mathrm{d}}{\to }N(0,1)$. Also, Markov's inequality and $\mathbb{E}{T}_n=n$ giveThus $B_n$ equals $(T_n-n)/\sqrt{n}$ outside an event of vanishing probability, so part (i) and Slutsky's theorem give $B_n\stackrel{\mathrm{d}}{\to }N(0,1)$. Exact independence of random variables for each $n$ now gives the joint limit $(A_n,B_n)\stackrel{\mathrm{d}}{\to }(Z_1,Z_2)$ with independent standard normal random variables.

Finally, $\mathbb{E}{C}_n=0$ and $\mathrm{Var}(C_n)=n^{-2}$, so Chebyshev's inequality gives $C_n\to 0$ in probability. The first coordinate in the question is $A_n+C_n$. The second differs from $B_n$ only on $\{T_n>n^2\}$, and so its difference tends to zero in probability. The vector version of Slutsky's theorem proves the required joint convergence in distribution. The boundary of $(-\infty ,x]\times [y,\infty )$ has zero limiting probability, since both marginal distributions have continuous cumulative distribution functions. The Portmanteau theorem therefore yieldsThis is $\mathbb{P}(Z_1\le x,Z_2\ge y)$ by independence of random variables.

There is a stronger conclusion than the requested limit. For $x>0$, the event $N_n-n\ge x\sqrt{n}$ forces $N_n\ge n+1$, because $N_n$ is an integer. In particular, at least $n$ arrivals have occurred by time $n$, so the $n$-th Poisson arrival time satisfies $T_n\le n$. This contradicts $T_n-n\ge y\sqrt{n}$ for $y>0$. Thus the two events are disjoint:

The unit-time increments $D_j=N_j-N_{j-1}$ are independent and identically distributed random variables, by independent increments and stationary increments of the Poisson process. Each has the Poisson distribution of parameter one, hence expected value one and variance one. The central limit theorem therefore gives

Set $T_0=0$ and $E_j=T_j-T_{j-1}$. The Poisson interarrival times $E_j$ are independent and identically distributed random variables with the rate-one exponential distribution. In particular, $\mathbb{E}{E}_j=1$ and $\mathrm{Var}(E_j)=1$, so the finite, positive variance hypothesis of the central limit theorem holds. Since $T_n={\sum }_{j=1}^n{E}_j$,