The method of indirect least squares (ILS) using a proxy for a discrete instrument is shown to identify a weighted average of local treatment effects. The weights are nonnegative if and only if the proxy is intensity preserving for the instrument. A similar result holds for instrumental...

The effect of measurement error on estimates of the Q and cash flow model of investment is investigated. Two sources of error are considered: expensing of research and development expenditures and the failure to separate out that component of cash flow which relaxes financing constraints. We...

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...

Consider an observed binary regressor D and an unobserved binary variable D*, both of which affect some other variable Y. This paper considers nonparametric identification and estimation of the effect of D on Y, conditioning on D*=0. For example, suppose Y is a person's wage, the unobserved D*...

We study the scope of local indirect least squares (LILS) methods for nonparametrically estimating average marginal effects of an endogenous cause X on a response Y in triangular structural systems that need not exhibit linearity, separability, or monotonicity in scalar unobservables. One main...

This paper proposes a new method of obtaining identification in mismeasured regressor models, triangular systems, and simultaneous equation systems. The method may be used in applications where other sources of identification such as instrumental variables or repeated measurements are not...

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...