This paper provides estimators of discrete choice models, including binary, ordered, and multinomial response (choice) models. The estimators closely resemble ordinary and two stage least squares. The distribution of the model's latent variable error is unknown and may be related to the...

This paper considers identification and estimation of the marginal effect of a mismeasured binary regressor in a nonparametric regression, or the conditional average effect of a binary treatment or policy on some outcome where treatment may be misclassified. Misclassification probabilities and...

We consider estimation of means of functions that are scaled by an unknown density, or equivalently, integrals of conditional expectations. The "ordered data" estimator we provide is root n consistent, asymptotically normal, and is numerically extremely simple, involving little more than...

We discuss the relative advantages and disadvantages of four types of convenient estimators of binary choice models when regressors may be endogenous or mismeasured, or when errors are likely to be heteroskedastic. For example, such models arise when treatment is not randomly assigned and...

This paper provides a few variants of a simple estimator for binary choice models with endogenous or mismeasured regressors, or with heteroskedastic errors. Unlike control function methods, which are generally only valid when endogenous regressors are continuous, the estimators proposed here can...

Misclassification in binary choice (binomial response) models occurs when the dependent variable is measured with error, that is, when an actual "one" response is sometimes recorded as a zero, and vice versa. This paper shows that binary choice models with misclassification are...

Let y be a vector endogenous variables and let w be a vector of covariates, parameters and errors or unobservables that together are assumed to determine y. A structural model y=H(y,w) is complete and coherent if it has a well defined reduced form, meaning that for any value of w there exists a...