A multi-step procedure to determine the number of factors in large approximate factor models
A multi-step procedure to determine the number of factors in large approximate factor models
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DOI：
10.1080/03610926.2019.1710752
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发表时间：
2020-01
期刊：
影响因子：
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通讯作者：
Ronghua Luo;Jiakun Jiang;Wei Lan;Chengliang Yan;Yue Ding
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文献类型：
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作者：
Ronghua Luo;Jiakun Jiang;Wei Lan;Chengliang Yan;Yue Ding
本文以Ahn and Horenstein（2013）的特征值比（ER）标准为精神，提出了一个多步骤程序来确定大型近似因子模型（Chamberlain and Rothschild 1983）中的公共因子数量。我们从理论上证明，在相对强弱因素同时存在的情况下，这一多步骤过程可以一致地识别出因子的真实数量，这是用ER准则很难做到的。大量的仿真结果表明，在某些情况下，与ER准则相比，该方法具有更好的有限样本特性。
Abstract We propose in this article a multi-step procedure to determine the number of common factors in large approximate factor models (Chamberlain and Rothschild 1983) in the spirit of the eigenvalue ratio (ER) criterion of Ahn and Horenstein (2013). We show theoretically that this multi-step procedure can consistently identify the true number of factors while both relatively strong and weak factors simultaneously exist, which is not easy to perform with the ER criterion. Our extensive simulation results demonstrate that the proposed procedure has better finite sample properties compared with that of the ER criterion under certain cases.