In this paper prediction-based estimating functions (PBEFs), introduced in Sørensen (2000), are reviewed and PBEFs for …

’s backward equation, neural networks and functions of our choice. Martingale estimating functions will be used to obtain …

estimating functions approach, which provides a convenient framework for deriving optimal inference for nonlinear time series … functions are optimal and are more informative than component estimating functions. The combined estimating functions approach … estimating functions provides a mechanism for fast estimation in the general case, and is illustrated using simulated data sets …

focus is on estimating functions and asymptotic results. Maximum likelihood estimation is briefly considered, but the … emphasis is on computationally less demanding martingale estimating functions. Particular attention is given to explicit … estimating functions. Results on both fixed frequency and high frequency asymptotics are given. When choosing among the many …

approximate martingale estimating functions and covers a large class of estimators including most of the pre- viously proposed …. Optimal martingale estimating functions in the sense of Godambe and Heyde are shown to be give rate optimal and efficient …

. Both have their origins in Engle (1982) and Bollerslev (1986). This paper uses the theory of estimating functions (EF) as a …