A Comparison of the Finite Sample Properties of Selection Rules of Factor Numbers in Large Datasets
A Comparison of the Finite Sample Properties of Selection Rules of Factor Numbers in Large Datasets
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大数据集中因子数选择规则的有限样本性质比较
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发表时间：
2013
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通讯作者：
Liang Guo
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作者：
Liang Guo
本文比较了在小样本情况下，动态因素模型中选择因素个数的主要标准的性质。研究了静态因素数和动态因素数的选取规则。仿真结果表明，Ahn和Horenstein(2013)提出的GR比和Onatski(2010)提出的准则优于其他准则。此外，这两个准则可以在动态因素设计中准确地选择静态因素的数量。此外，Hallin和Liska(2007)和Breitung和Pigorsch(2009)提出的标准在大多数情况下正确地选择了动态因素的数量。然而，经验应用表明，大多数标准在存在一个强大因素的情况下只选择一个因素。
In this paper, we compare the properties of the main criteria proposed for selecting the number of factors in dynamic factor model in a small sample. Both static and dynamic factor numbers' selection rules are studied. Simulations show that the GR ratio proposed by Ahn and Horenstein (2013) and the criterion proposed by Onatski (2010) outperform the others. Furthermore, the two criteria can select accurately the number of static factors in a dynamic factors design. Also, the criteria proposed by Hallin and Liska (2007) and Breitung and Pigorsch (2009) correctly select the number of dynamic factors in most cases. However, empirical applications show most criteria select only one factor in presence of one strong factor.