Computational Issues in the Sequential Probit Model: A Monte Carlo Study
Computational Issues in the Sequential Probit Model: A Monte Carlo Study
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序贯概率模型中的计算问题：蒙特卡罗研究
DOI：
10.2139/ssrn.555081
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发表时间：
2004
影响因子：
2
通讯作者：
P. Waelbroeck
中科院分区：
文献类型：
--
作者：
P. Waelbroeck
我们讨论了序贯概率模型中限制其在应用研究中使用的计算问题。我们用模拟最大似然(SML)方法和贝叶斯MCMC算法估计模型的参数。我们提供了关于两种估计器的相对性能的蒙特卡罗证据，并发现SML过程计算的是不太可靠的估计相关系数的标准误差。考虑到与估计过程相关的数值困难，我们建议应用研究人员同时使用模拟最大似然方法中的随机优化算法和贝叶斯MCMC算法来检查结果的兼容性。
We discuss computational issues in the sequential probit model that have limited its use in applied research. We estimate parameters of the model by the method of simulated maximum likelihood (SML) and by Bayesian MCMC algorithms. We provide Monte Carlo evidence on the relative performance of both estimators and find that the SML procedure computes standard errors of the estimated correlation coefficients that are less reliable. Given the numerical difficulties associated with the estimation procedures, we advise the applied researcher to use both the stochastic optimization algorithm in the Simulated Maximum Likelihood approach and the Bayesian MCMC algorithm to check the compatibility of the results.