Variable selection, estimation and application of dynamic short panel data models with time-varying individual fixed effects

被引：0
|
作者：
Sun Y. [1 ]
Huang W. [1 ]
机构：
[1] School of Economics, Shanghai University of Finance and Economics, Shanghai
来源：
Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice | 2022年 / 42卷 / 06期
基金：
中国国家自然科学基金;
关键词：
Conditional quasilikelihood; ECM algorithm; Mundlak-Chamberlain projection; Time-varying individual fixed effects;
D O I：
10.12011/SETP2021-3018
中图分类号：
学科分类号：
摘要：
Although the time dimension of common micro panel data is short, it often has a long time span, which may lead to incorrect inference based on the usual fixed effects panel data model. However, panel data model with time-varying individual fixed effect can alleviate model specification error. We adopt the modified Mundlak-Chamberlain projection which enables us to identify the influence of time invariant individual explanatory variables, such as gender discrimination in wages. Moreover, the effects of observed explanatory variables may also be time-varying due to external environmental factors such as policies. This, together with the above projection will greatly increase the number of parameters, and reduce the accuracy of estimation. Therefore, we study variable selection and introduce the constraint conditions of the projection parameters. The conditional penalized quasi-likelihood method and ECM algorithm are used to obtain selection and estimation simultaneously. Simulation studies show that our methods perform well. Finally, we identify the influence of different executive incentives (including salary incentive and equity incentive) on the motivation of corporate financial asset allocation by our procedures. © 2022, Editorial Board of Journal of Systems Engineering Society of China. All right reserved.
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页码：1423 / 1433
页数：10
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