A maximum entropy approach to estimation and inference in dynamic models

Two estimation problems constructed from dynamic discrete time models are examined. One problem concerns noisy state observations in a case where there is nonlinearity between the observation and state equations. In this problem, the goal is to estimate the observation and state equation parameters and the unknown values of the state variable. The other problem concerns a case of linear-quadratic control where the objective is to estimate state equation and objective function parameters. In both cases, a nonlinear inversion procedure is proposed for estimating unknown parameters.