Bayesian analysis of heavy-tailed Heckman selection models using Hamiltonian Monte Carlo

Key Points

• The study uncovers robust estimates in heavy-tailed models, enhancing statistical inference.
• Key evidence includes high efficiency achieved through Hamiltonian Monte Carlo methods.
• Observational analysis applies a Bayesian approach to Heckman selection models with heavy-tailed distributions.
• The findings highlight the effectiveness of novel statistical methods, inviting further exploration.