DIFFERENCES BETWEEN CLASSICAL AND BAYESIAN ESTIMATES FOR MIXED LOGIT MODELS: A REPLICATION STUDY
DIFFERENCES BETWEEN CLASSICAL AND BAYESIAN ESTIMATES FOR MIXED LOGIT MODELS: A REPLICATION STUDY
复制标题
DOI：
10.1002/jae.2513
复制
发表时间：
2017-03-01
影响因子：
2.1
通讯作者：
Boztug, Yasemin
中科院分区：
文献类型：
--
作者：
Elshiewy, Ossama;Zenetti, German;Boztug, Yasemin
混合logit模型在应用计量经济学中有着广泛的应用。研究人员通常依赖于经典和贝叶斯估计方法之间的自由选择。然而，他们的参数估计的相似性的经验证据是稀疏的。假定的相似性主要基于分析单个数据集的一个经验研究（Huber J，Train KE. 2001.关于个体平均部分值的经典估计和贝叶斯估计的相似性。Marketing Letters 12（3）：259-269）。我们的复制研究提供了一个概括的结果，比较经典和贝叶斯参数估计从六个额外的数据集，特别是面板与横截面数据。在一般情况下，我们的研究结果表明，这两种方法提供了类似的结果，与面板数据相比，横截面数据的相似性较小。版权所有（C）2016约翰威利父子有限公司
The mixed logit model is widely used in applied econometrics. Researchers typically rely on the free choice between the classical and Bayesian estimation approach. However, empirical evidence of the similarity of their parameter estimates is sparse. The presumed similarity is mainly based on one empirical study that analyzes a single dataset (Huber J, Train KE. 2001. On the similarity of classical and Bayesian estimates of individual mean partworths. Marketing Letters 12(3): 259-269). Our replication study offers a generalization of their results by comparing classical and Bayesian parameter estimates from six additional datasets and specifically for panel versus cross-sectional data. In general, our results suggest that the two methods provide similar results, with less similarity for cross-sectional data than for panel data. Copyright (C) 2016 John Wiley & Sons, Ltd.