Fast robustness quantification with variational Bayes
This work addresses the need for efficient robustness quantification in Bayesian modeling for economics, though it appears incremental as it applies existing methods to a specific domain.
The paper tackled the problem of quickly measuring the robustness of posterior expectations in Bayesian hierarchical models to alternative prior choices, using variational Bayes and linear response methods, with an application to microcredit effectiveness in developing countries, achieving fast and accurate results.
Bayesian hierarchical models are increasing popular in economics. When using hierarchical models, it is useful not only to calculate posterior expectations, but also to measure the robustness of these expectations to reasonable alternative prior choices. We use variational Bayes and linear response methods to provide fast, accurate posterior means and robustness measures with an application to measuring the effectiveness of microcredit in the developing world.