Predictive likelihood for Bayesian model selection and averaging
Predictive likelihood for Bayesian model selection and averaging
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DOI：
10.1016/j.ijforecast.2009.08.001
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发表时间：
2010-10-01
影响因子：
7.9
通讯作者：
Tsay, Ruey
中科院分区：
文献类型：
--
作者：
Ando, Tomohiro;Tsay, Ruey
本文研究了贝叶斯模型预测分布的性能。为了克服预测似然评估的困难，我们在贝叶斯模型中引入了期望对数预测似然的概念，并提出了期望对数预测似然的估计量。估计量是通过校正预测分布的对数似然的渐近偏差作为其期望值的估计而得到的。研究了该准则与传统信息准则的关系，表明该准则是传统信息准则的自然延伸。然后开发了一种新的模型选择标准和一种新的模型平均方法，其中单个模型的权重取决于它们的期望对数预测可能性。我们使用蒙特卡罗实验和一个真实的例子来检验所提出的方法的性能，该方法涉及G7国家实际国内生产总值季度增长率的预测。样本外预测表明，所提出的方法优于文献中可用的其他方法。(C) 2009年国际预报员协会。Elsevier B.V.版权所有。
This paper investigates the performance of the predictive distributions of Bayesian models. To overcome the difficulty of evaluating the predictive likelihood, we introduce the concept of expected toe-predictive likelihoods for Bayesian models, and propose an estimator of the expected log-predictive likelihood. The estimator is derived by correcting the asymptotic bias of the log-likelihood of the predictive distribution as an estimate of its expected value. We investigate the relationship between the proposed criterion and the traditional information criteria and show that the proposed criterion is a natural extension of the traditional ones. A new model selection criterion and a new model averaging method are then developed, with the weights for the individual models being dependent on their expected log-predictive likelihoods. We examine the performance of the proposed method using Monte Carlo experiments and a real example, which concerns the prediction of quarterly growth rates of real gross domestic product in the G7 countries. Out-of-sample forecasts show that the proposed methodology outperforms other methods available in the literature. (C) 2009 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.