Semi-parametric estimation for the Box-Cox transformation model with partially linear structure

被引:0
|
作者
ZHOU GuoLiang [1 ]
ZHOU YaHong [2 ]
机构
[1] Institute of Accounting and Finance, Shanghai University of Finance and Economics
[2] School of Economics, Shanghai University of Finance and Economics
基金
中国国家自然科学基金;
关键词
Box-Cox transformation model; semiparametric estimation; rank condition; smoothed kernel;
D O I
暂无
中图分类号
O212.7 [非参数统计];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
The Box-Cox transformation model has been widely used in applied econometrics, positive accounting, positive finance and statistics. There is a large literature on Box-Cox transformation model with linear structure. However, there is seldom seen on the discussion for such a model with partially linear structure. Considering the importance of the partially linear model, in this paper, a relatively simple semi-parametric estimation procedure is proposed for the Box-Cox transformation model without presuming the linear functional form and without specifying any parametric form of the disturbance, which largely reduces the risk of model misspecification. We show that the proposed estimator is consistent and asymptotically normally distributed. Its covariance matrix is also in a closed form, which can be easily estimated. Finally, a simulation study is conducted to see the finite sample performance of our estimator.
引用
收藏
页码:459 / 481
页数:23
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