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On the Estimation and Inference of Global Categorical Effects in General Parametric Models: With a Logit Regression Application

In this paper we propose a method for estimating and conducting inference on categorical effects of random variables that are characterize by more that two categories. We focus on a class of parametric asymptotically normal estimators in deriving the the properties which allows for inference on the proposed categorical effects. Existing methods prescribe pairwise within categories or sub-sampling comparisons. The proposed method allows for global comparison between each mutually exclusive category relative to a global evaluation of the other categories, while allowing the remainder of the regressors in the model to be evaluated unconditional on the category of interest. We also provide an example and a Monte Carlo simulation in the context of the Logit model.