Journal of Undergraduate Research


nonparametric inference, binary endogenous regressor, hypergeometric distribution


Family, Home, and Social Sciences




When there is endogeneity in an economic model, basic ordinary least squares regression analysis breaks down. Our assumptions for the model collapse so we cannot infer causality without bias in our estimations. Therefore, use of an instrumental variable is necessary. However, if instruments are weak, sample sizes are small, or assumptions about error terms are invalid, then our analysis is biased as well. We developed an exact, finite-sample approach to instrumental variables estimation and inference that remains valid for weak instruments, small samples, and other settings where large-sample approximations are poor. This approach imposed no parametric model for causal effects and made no distributional assumptions on the outcome variable. We estimated effects of a possibly endogenous binary regressor using an instrumental variable.

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