Estimation of Dynamic Bivariate Mixture Models
比较了贝叶斯MCMC和基于高效重要性抽样的最大似然法在估计动态双变量混合模型上的表现，发现后者更准确且结果与已有研究显著不同。
This note compares a Bayesian Markov chain Monte Carlo approach implemented by Watanabe with a maximum likelihood ML approach based on an efficient importance sampling procedure to estimate dynamic bivariate mixture models. In these models, stock price volatility and trading volume are jointly directed by the unobservable number of price-relevant information arrivals, which is specified as a serially correlated random variable. It is shown that the efficient importance sampling technique is extremely accurate and that it produces results that differ significantly from those reported by Watanabe.