We study a regression model with a binary explanatory variable that is subject to misclassification errors. The regression coefficient is then only partially identified. We derive several results that relate different assumptions about the misclassification probabilities and the conditional...

This paper derives the LIML estimator for a spatial autoregressive model with endogenous regressors in the presence of many instruments. The LIML estimator is consistent when the number of instruments increases at a slower rate relative to the sample size. Due to spatial correlation, the LIML...

This work considers the estimation of a network model with sampled networks. Chandrasekhar and Lewis (2011) show that the estimation with sampled networks could be biased due to measurement error induced by sampling and propose a bias correction by restricting the estimation to sampled nodes to...