A new type of stochastic dependence for a sequence of random variables is introduced and studied. Precisely, (Xn)n≥1 is said to be conditionally identically distributed (c.i.d.), with respect to a filtration
$(\mathcal{G}_{n})_{n\geq 0}$
, if it is adapted to
$(\mathcal{G}_{n})_{n\geq 0}$
and, for each n≥0, (Xk)k>n is identically distributed given the past
$\mathcal{G}_{n}$
. In case
$\mathcal{G}_{0}=\{\varnothing,\Omega\}$
and
$\mathcal{G}_{n}=\sigma(X_{1},\ldots,X_{n})$
, a result of Kallenberg implies that (Xn)n≥1 is exchangeable if and only if it is stationary and c.i.d. After giving some natural examples of nonexchangeable c.i.d. sequences, it is shown that (Xn)n≥1 is exchangeable if and only if (Xτ(n))n≥1 is c.i.d. for any finite permutation τ of {1,2,…}, and that the distribution of a c.i.d. sequence agrees with an exchangeable law on a certain sub-σ-field. Moreover, (1/n)∑k=1nXk converges a.s. and in L1 whenever (Xn)n≥1 is (real-valued) c.i.d. and E[|X1|]<∞. As to the CLT, three types of random centering are considered. One such centering, significant in Bayesian prediction and discrete time filtering, is
$E[X_{n+1}\vert \mathcal{G}_{n}]$
. For each centering, convergence in distribution of the corresponding empirical process is analyzed under uniform distance.