This paper provides inference methods for best linear approximations to functions which are known to lie within a band.

This paper studies identification and estimation in a binary response model with random coefficients B allowed to be correlated with regressors X.The objective is to identifiy the mean of the distribution of B and estimate a trimmed mean of this distribution.

Virtually all methods aimed at correcting for covariate measurement error in regressions rely on some form of additional information (e.g. validation data, known error distributions, repeated measurements or instruments). In contrast, we establish that the fully nonparametric classical errors-in-variables mode is identifiable from data on the regressor and the dependent variable alone, unless the model takes a very specific parametric form.

In this paper we study nonparametric estimation in a binary treatment model where the outcome equation is of unrestricted form, and the selection equation contains multiple unobservables that enter through a nonparametric random coefficients specification.

This paper considers nonparametric identifiation of a two-stage entry and bidding model for auctions which we call the Affiliated-Signal (AS) Model.

In parametric models a sufficient condition for local idenfication is that the vector of moment is differentiable at the true parameter with full rank derivative matrix. This paper shows that additional conditions are often needed in nonlinear, nonparametric models to avoid nonlinearities overwhelming linear effects.

In this paper, the author constructs a new test of conditional moment inequalities based on studentised kernel estimates of moment functions.

This paper develops a general nonparametric framework for testing monotonicity of a regression function.

In this paper the authors study a random coefficient model for a binary outcome.