Adam M. Rosen

Identification With Imperfect Instruments

Dealing with endogenous regressors is a central challenge of applied research. The standard solution is to use instrumental variables that are assumed to be uncorrelated with unobservables. We instead allow the instrumental variable to be correlated with the error term, but we assume the correlation between the instrumental variable and the error term has the same sign as the correlation between the endogenous regressor and the error term and that the instrumental variable is less correlated with the error term than is the endogenous regressor. Using these assumptions, we derive analytic bounds for the parameters. We demonstrate that the method can generate useful (set) estimates by using it to estimate demand for differentiated products.

An instrumental variable model of multiple discrete choice: IV model of multiple discrete choice

This paper studies identification in multiple discrete choice models in which there may be endogenous explanatory variables, that is, explanatory variables that are not restricted to be distributed independently of the unobserved determinants of latent utilities. The model does not employ large support, special regressor, or control function restrictions; indeed, it is silent about the process that delivers values of endogenous explanatory variables, and in this respect it is incomplete. Instead, the model employs instrumental variable restrictions that require the existence of instrumental variables that are excluded from latent utilities and distributed independently of the unobserved components of utilities. We show that the model delivers set identification of latent utility functions and the distribution of unobserved heterogeneity, and we characterize sharp bounds on these objects. We develop easy-to-compute outer regions that, in parametric models, require little more calculation than what is involved in a conventional maximum likelihood analysis. The results are illustrated using a model that is essentially the conditional logit model of McFadden (1974), but with potentially endogenous explanatory variables and instrumental variable restrictions.

An instrumental variable random coefficients model for binary outcomes

In this paper, we study a random-coefficients model for a binary outcome. We allow for the possibility that some or even all of the explanatory variables are arbitrarily correlated with the random coefficients, thus permitting endogeneity. We assume the existence of observed instrumental variables Z that are jointly independent with the random coefficients, although we place no structure on the joint determination of the endogenous variable X and instruments Z, as would be required for a control function approach. The model fits within the spectrum of generalized instrumental variable models, and we thus apply identification results from our previous studies of such models to the present context, demonstrating their use. Specifically, we characterize the identified set for the distribution of random coefficients in the binary response model with endogeneity via a collection of conditional moment inequalities, and we investigate the structure of these sets by way of numerical illustration.