Sample Partitioning Estimation for Ergodic Diffusions

In this paper we present a new technique to obtain estimators for parameters of ergodic processes. When a diffusion is ergodic its transition density converges to the invariant density [1]. This convergence enabled us to introduce a sample partitioning technique that gives, in each sub-sample, observations that can be treated as independent and identically distributed. Within this framework, is possible the construction of estimators like maximum likelihood estimators or others. Consistency; Ergodic Diffusions; Independency; Least Squares; Martingale Estimating Functions; Maximum Likelihood Estimators; Method of Moments; Transition and Invariant Densities.