  In the first part of this thesis, we study a Markov chain on $\mathbb{R}_+
\times S$, where $\mathbb{R}_+$ is the non-negative real numbers and $S$ is a
finite set, in which when the $\mathbb{R}_+$-coordinate is large, the
$S$-coordinate of the process is approximately Markov with stationary
distribution $\pi_i$ on $S$. Denoting by $\mu_i(x)$ the mean drift of the
$\mathbb{R}_+$-coordinate of the process at $(x,i) \in \mathbb{R}_+ \times S$,
we give an exhaustive recurrence classification in the case where $\sum_{i}
\pi_i \mu_i (x) \to 0$, which is the critical regime for the
recurrence-transience phase transition. If $\mu_i(x) \to 0$ for all $i$, it is
natural to study the Lamperti case where $\mu_i(x) = O(1/x)$; in that case the
recurrence classification is known, but we prove new results on existence and
non-existence of moments of return times. If $\mu_i (x) \to d_i$ for $d_i \neq
0$ for at least some $i$, then it is natural to study the generalized Lamperti
case where $\mu_i (x) = d_i + O (1/x)$. By exploiting a transformation which
maps the generalized Lamperti case to the Lamperti case, we obtain a recurrence
classification and an existence of moments result for the former.
  In the second part of the thesis, for a random walk $S_n$ on $\mathbb{R}^d$
we study the asymptotic behaviour of the associated centre of mass process $G_n
= n^{-1} \sum_{i=1}^n S_i$. For lattice distributions we give conditions for a
local limit theorem to hold. We prove that if the increments of the walk have
zero mean and finite second moment, $G_n$ is recurrent if $d=1$ and transient
if $d \geq 2$. In the transient case we show that $G_n$ has diffusive rate of
escape. These results extend work of Grill, who considered simple symmetric
random walk. We also give a class of random walks with symmetric heavy-tailed
increments for which $G_n$ is transient in $d=1$.
