A maximum likelihood estimator for models containing nuisance parameters is proposed. The estimator is shown to be asymptotically normal and attain the semiparametric efficiency bounds for a number of important econometric models. The idea is to find a parametric model that passes through the true model. The score for the parametric model is then estimated nonparametrically and the estimator is obtained by setting the estimated score to zero.

We propose an estimation method for models of conditional moment restrictions, which contain finite dimensional unknown parameters (theta) and infinite dimensional unknown functions (h). Our proposal is to approximate h with a sieve and to estimate theta and the sieve parameters jointly by applying the method of minimum distance. We show that: (i) the sieve estimator of h is consistent with a rate faster than n-super--1/4 under certain metric; (ii) the estimator of theta is root-n consistent and asymptotically normally distributed; (iii) the estimator for the asymptotic covariance of the theta estimator is consistent and easy to compute; and (iv) the optimally weighted minimum distance estimator of theta attains the semiparametric efficiency bound. We illustrate our results with two examples: a partially linear regression with an endogenous nonparametric part, and a partially additive IV regression with a link function. Copyright The Econometric Society 2003.

Hans van Ophem (1993) employed a switching regression model with earnings entering the choice equation to investigate earnings differentials between the public and private sectors. Ophem also described a 'modified switching regression model' in which only the equation for unknown earnings is substituted into the choice equation. He argued that the coefficients in the choice equation can be identified in the modified switching regression model without exclusion restrictions and that estimates from the modified switching regression model are more efficient than estimates from the standard switching regression model. The authors show that there are flaws in Ophem's analysis which invalidate his claims.