Counts With An Endogenous Binary Regressor: A Series Expansion Approach
We propose an estimator for count data regression models where a binary regressor is endogenously determined. This estimator departs from previous approaches by using a flexible form for the conditional probability function of the counts. Using a Monte Carlo experiment we show that our estimator improves the fit and provides a more reliable estimate of the impact of regressors on the count when compared to alternatives which do restrict the mean to be linear-exponential. In an application to the number of trips by households in the US, we find that the estimate of the treatment effect obtained is considerably different from the one obtained under a linear-exponential mean specification.
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