4.28 Estimation of Some Partially Specified Nonlinear Models
This paper presents a procedure for analyzing a partially specified
nonlinear regression model in which the nuisance parameter is an
unrestricted function of a subset of regressors. The procedure does
not require parametric modeling of the nuisance parameter but assumes
that the model can be transformed into a partially specified linear
equation by inverting some nonlinear functions. The model parameters
are estimated by applying Robinson's (1988a) procedure and the
estimator is shown to be root-N-consistent and asymptotically normal.
One attraction of the estimator is that is that it is computationally
simple, requiring no more than least squares regressions. A
simulation study indicates that the estimator has practical values.