Model selection for factor analysis: Some new criteria and performance comparisons
Model selection for factor analysis: Some new criteria and performance comparisons
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DOI：
10.1080/07474938.2017.1382763
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发表时间：
2019-07-03
影响因子：
1.2
通讯作者：
Jeong, Hanbat
中科院分区：
文献类型：
--
作者：
Choi, In;Jeong, Hanbat
本文推导了假设大量横断面观测数据的近似因子模型的Akaike信息准则(AIC)、修正的AIC、贝叶斯信息准则(BIC)以及Hannan和Quinn的信息准则，并研究了这些信息准则的一致性性质。它还报告了广泛的模拟结果，比较了现有的和新的选择因素数量的程序的性能。仿真结果表明，该算法能够很好地确定哪一种准则的性能最好。在实践中，建议同时考虑几个准则，特别是Hannan和Quinn的信息准则，Bai和Ng的ICp2和BIC3，以及Onatski和Ahn和Horenstein的基于特征值的准则。本文所考虑的模型选择标准也适用于Stock和Watson的两个宏观经济数据集。根据使用的模型选择标准，结果差别很大，但可以获得证据，表明第一个数据有五个因素，第二个数据有五到七个因素。
This paper derives Akaike information criterion (AIC), corrected AIC, the Bayesian information criterion (BIC) and Hannan and Quinn's information criterion for approximate factor models assuming a large number of cross-sectional observations and studies the consistency properties of these information criteria. It also reports extensive simulation results comparing the performance of the extant and new procedures for the selection of the number of factors. The simulation results show the dixfb03;culty of determining which criterion performs best. In practice, it is advisable to consider several criteria at the same time, especially Hannan and Quinn's information criterion, Bai and Ng's ICp2 and BIC3, and Onatski's and Ahn and Horenstein's eigenvalue-based criteria. The model-selection criteria considered in this paper are also applied to Stock and Watson's two macroeconomic data sets. The results differ considerably depending on the model-selection criterion in use, but evidence suggesting five factors for the first data and five to seven factors for the second data is obtainable.