No abstract

This paper explains why computing the marginal effect of a change in two variables is more complicated in nonlinear models than in linear models. The command inteff computes the correct marginal effect of a change in two interacted variables for a logit or probit model, as well as the correct standard errors. The inteff command graphs the interaction effect and saves the results to allow further investigation.

We propose an estimation method for models of conditional moment restrictions, which contain finite dimensional unknown parameters (θ) and infinite dimensional unknown functions (h). Our proposal is to approximate h with a sieve and to estimate θ 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 −1/4 under certain metric; (ii) the estimator of θ is √ n consistent and asymptotically normally distributed; (iii) the estimator for the asymptotic covariance of the θ estimator is consistent and easy to compute; and (iv) the optimally weighted minimum distance estimator of θ 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.