A1140
Title: Synthetic control via Bayesian variable selection with a soft simplex constraint
Authors: Quan Zhou - Texas A&M University (United States) [presenting]
Abstract: Whether the synthetic control method should be implemented with the simplex constraint and how to implement it in a high-dimensional setting, have been widely discussed. To address both issues simultaneously, a novel Bayesian synthetic control method that integrates a soft simplex constraint with spike-and-slab variable selection is proposed. The model features a hierarchical prior capturing how well the data aligns with the simplex assumption, which enables the method to efficiently adapt to the structure and information contained in the data. The main theoretical contribution is a high-dimensional selection consistency result for the model under the simplex constraint. To compute the posterior distribution, a novel Metropolis-within-Gibbs sampler is proposed that updates the regression coefficients of two predictors simultaneously from their full conditional posterior distribution, which has an explicit but complicated expression. Simulation studies demonstrate that the method performs well across a wide range of settings in terms of both variable selection and treatment effect estimation, even when the true data-generating process does not adhere to the simplex constraint. Finally, the application of the method to two empirical examples in the economic literature yields interesting insights into the impact of economic policies.
