This paper considers identification and estimation of the effect of a mismeasured binary regressor in a nonparametric or semiparametric regression, or the conditional average effect of a binary treatment or policy on some outcome where treatment may be misclassified. Failure to account for misclassification is shown to result in attenuation bias in the estimated treatment effect. An identifying assumption that overcomes this bias is the existence of an instrument for the binary regressor that is conditionally independent of the treatment effect. A discrete instrument suffices for nonparametric identification.

A simple root n consistent, asymptotically normal semiparametric estimator of the coefficient vector beta in the latent variable specification y = L (beta'x + e) is constructed. The distribution of e is unknown and may be correlated with x or be conditionally heteroscedastic, e.g., x can contain measurement error. The function L can also be unknown. The identification assumption is that e is uncorrelated with instruments u and that the conditional distribution of e given x and u does not depend on one of the regressors, which has some special properties. Extensions to more general latent variable specifications are provided.

Arthur Lewbel, Constructing Instruments for Regressions With Measurement Error When no Additional Data are Available, with An Application to Patents and R&D, Econometrica, Vol. 65, No. 5 (Sep., 1997), pp. 1201-1213

W. M. Gorman's (1981) concept of Engel curve "rank" is extended to apply to any demand system. Rank is shown to have implications for specification, separability, and aggregation of demands. A simple nonparametric test of rank using Engel curve data is described and applied to U.S. and U.K. consumer survey data. The test employs a new general method for testing the rank of estimated matrices. The results are used to assess theoretical and empirical aggregation error in representative consumer models, and to explain a representative consumer paradox. Copyright 1991 by The Econometric Society.

This paper characterizes all utility derived demand systmes having Engel curves that are linear in both income and an arbitrary function of income. This class encompasses virtually all utility derived demand systems that have been estimated in the past using ag gregate data with explicit treatment of the problem of aggregation ac ross individuals. It includes extensions of the PIGLOG and PIGL class es that have similar properties to these classes, but allow for more general Engel curve shapes. This paper extends W. M. Gorman's study o f these forms primarily by characterizing systems of rank two. The ap plication of the characterized systems to problems of nesting, separa bility, flexibility, Engel curve analysis, estimation, and aggregatio n are briefly discussed. Copyright 1987 by The Econometric Society.

For vectors z and w and scalar v, let r(v, z, w) be a function that can be nonparametrically estimated consistently and asymptotically normally, such as a distribution, density, or conditional mean regression function. We provide consistent, asymptotically normal nonparametric estimators for the functions G and H, where r(v, z, w) = H[vG(z), w], and some related models. This framework encompasses homothetic and homothetically separable functions, and transformed partly additive models r(v, z, w) = h[v + g(z), w] for unknown functions gand h Such models reduce the curse of dimensionality, provide a natural generalization of linear index models, and are widely used in utility, production, and cost function applications. We also provide an estimator of Gthat is oracle efficient, achieving the same performance as an estimator based on local least squares when H is known. Copyright The Econometric Society 2007.

This paper proposes new estimators of the latent regression function in nonparametric censored and truncated regression models. Our estimators are computationally convenient, consisting only of two nonparametric regressions and a univariate integral. We establish consistency and asymptotic normality for an implementation based on local linear kernel estimators. An extension permits estimation in the presence of a general form of heteroscedasticity.