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Determining the number of factors in constrained factor models via Bayesian information criterion

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成果类型:
期刊论文
作者:
Xiang, Jingjie;Guo, Gangzheng;Li, Jiaolong
通讯作者:
Jiaolong Li
作者机构:
[Xiang, Jingjie] Cent China Normal Univ, Sch Econ & Business Adm, Wuhan, Peoples R China.
[Guo, Gangzheng] China Construct Bank, Beijing, Peoples R China.
[Li, Jiaolong] Zhongnan Univ Econ & Law, Sch Stat & Math, Wuhan, Peoples R China.
通讯机构:
[Jiaolong Li] S
School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, China
语种:
英文
关键词:
Constrained factor models;principal components;least squares estimation;information criteria;housing prices;C2;C3;C5;G1
期刊:
Econometric Reviews
ISSN:
0747-4938
年:
2023
卷:
42
期:
1
页码:
98-122
基金类别:
This work was supported by the National Natural Science Foundation of China under Grant 72003197; and the Humanities and Social Science Fund of Ministry of Education of China under Grant 20YJC790058.
机构署名:
本校为第一机构
院系归属:
经济与工商管理学院
摘要:
This paper estimates the number of factors in constrained and partially constrained factor models (Tsai and Tsay, 2010) based on constrained Bayesian information criterion (CBIC). Following Bai and Ng (2002), the estimation of the number of factors depends on the tradeoff between good fit and parsimony, so we first derive the convergence rate of constrained factor estimates under the framework of large cross-sections (N) and large time dimensions (T). Furthermore, we demonstrate that the penalty for overfitting can be a function of N alone, so ...

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