Fast Posterior Sampling in Tightly Identified SVARs Using 'Soft' Sign Restrictions
利用“软”符号限制加速紧识别 SVAR 的后验抽样
We propose algorithms for conducting Bayesian inference in structural vector autoregressions identified using sign restrictions. The key feature of our approach is a sampling step based on 'soft' sign restrictions. This step draws from a target density that smoothly penalises parameter values that violate the restrictions, facilitating the use of computationally efficient Markov chain Monte Carlo sampling algorithms. An importance-sampling step yields draws conditional on…