  Asymptotic statistical theory for estimating functions is reviewed in a
generality suitable for stochastic processes. Conditions concerning existence
of a consistent estimator, uniqueness, rate of convergence, and the asymptotic
distribution are treated separately. Our conditions are not minimal, but can be
verified for many interesting stochastic process models. Several examples
illustrate the wide applicability of the theory and why the generality is
needed.
