High Dimensional Discrete Choice Models with Interactive Fixed Effects Applied to Causal Inference

We propose a two-step procedure to estimate a high dimensional discrete choice panel with interactive fixed effects where the initial and final estimators are obtained via a nuclear-norm regularized (NNR) maximum likelihood estimation and post-NNR iterated estimation, respectively. We apply the method to make counterfactual predictions of choice probabilities. Simulations demonstrate nice finite sample performance in estimation and tests. An illustrative application highlights the practical usefulness of our approach, revealing that the stock return of Fantasia Holdings Group Company Limited did experience a significant directional change following the 2021 credit rating downgrade event.