Smoothly mixing regressions
Smoothly mixing regressions
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DOI：
10.1016/j.jeconom.2006.05.022
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发表时间：
2007-05-01
影响因子：
6.3
通讯作者：
Keane, Michael
中科院分区：
文献类型：
--
作者：
Geweke, John;Keane, Michael
本文扩展了传统的贝叶斯正态混合模型，允许状态概率依赖于观测协变量。的依赖性是捕获一个简单的多项式概率模型。一个传统的和快速混合MCMC算法提供了访问后验分布在适度的计算成本。如本文插图所示，该模型与现有的计量经济模型具有竞争力。第一个例子研究了男性收入分布的分位数，以年龄和教育为条件，并表明，平稳混合回归是一个有吸引力的替代nonBayesian分位数回归。第二个例子模拟了标准普尔500指数收益率的序列相关性，并表明该模型与使用样本外似然标准的模型相比毫不逊色。(c)2006 Elsevier B.V.保留所有权利。
This paper extends the conventional Bayesian mixture of normals model by permitting state probabilities to depend on observed covariates. The dependence is captured by a simple multinomial probit model. A conventional and rapidly mixing MCMC algorithm provides access to the posterior distribution at modest computational cost. This model is competitive with existing econometric models, as documented in the paper's illustrations. The first illustration studies quantiles of the distribution of earnings of men conditional on age and education, and shows that smoothly mixing regressions are an attractive alternative to nonBayesian quantile regression. The second illustration models serial dependence in the S&P 500 return, and shows that the model compares favorably with ARCH models using out of sample likelihood criteria. (c) 2006 Elsevier B.V. All rights reserved.