J. S. Buzas Department of Statistics North Carolina State University Raleigh, NC 27695 We describe an approach to instrumental variable estimation in binary regression measurement error models. The method entails constructing an approximate mean model for the binary response as a function of the measured predictor, the instrument and any covariates in the model. Estimates are obtained by exploi...

Abstract. Application of least squares and instrumental variables to recovering parameters of nonlinear complex dynamic block-oriented systems is examined. For a system with the Hammerstein-Wiener structure the instrumental variable algorithm is designed and compared with the least squares algorithm for estimating system parameters. The advantages of the proposed instrumental variable estimator...