In this paper we consider a unified framework for parameter estimation problems. Under this framework, the unknown parameters appear in a linear fractional transformation (LIFT). A key advantage of the LIFT problem formulation is that it allows us to efficiently compute gradients, Hessians, and Gauss-Newton directions for general parameter estimation problems without resorting to inefficient finite-difference approximations. The generality of this approach also allows us to consider issues such as identifiability, persistence of excitation, and convergence for a large class of model structures under a single unified framework. (C) 2008 Elsevier Ltd. All rights reserved.