Penalized empirical likelihood for partially linear errors-in-variables panel data models with fixed effects

For the partially linear errors-in-variables panel data models with fixed effects, we, in this paper, study asymptotic distributions of a corrected empirical log-likelihood ratio and maximum empirical likelihood estimator of the regression parameter. In addition, we propose penalized empirical likel...

Full description

Saved in:
Bibliographic Details
Published in:Statistical papers (Berlin, Germany) Germany), 2020-12, Vol.61 (6), p.2351-2381
Main Authors: He, Bang-Qiang, Hong, Xing-Jian, Fan, Guo-Liang
Format: Article
Language:English
Subjects:
Asymptotic properties
Computer simulation
Data models
Economic models
Economic Theory/Quantitative Economics/Mathematical Methods
Economics
Estimating techniques
Estimators
Finance
Insurance
Likelihood ratio
Longitudinal studies
Management
Mathematical models
Mathematics and Statistics
Null hypothesis
Operations Research/Decision Theory
Parameter estimation
Penalty function
Probability Theory and Stochastic Processes
Regression coefficients
Regular Article
Statistics
Statistics for Business
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:For the partially linear errors-in-variables panel data models with fixed effects, we, in this paper, study asymptotic distributions of a corrected empirical log-likelihood ratio and maximum empirical likelihood estimator of the regression parameter. In addition, we propose penalized empirical likelihood (PEL) and variable selection procedure for the parameter with diverging numbers of parameters. By using an appropriate penalty function, we show that PEL estimators have the oracle property. Also, the PEL ratio for the vector of regression coefficients is defined and its limiting distribution is asymptotically chi-square under the null hypothesis. Moreover, empirical log-likelihood ratio for the nonparametric part is also investigated. Monte Carlo simulations are conducted to illustrate the finite sample performance of the proposed estimators.
ISSN:0932-5026
1613-9798
DOI:10.1007/s00362-018-1049-2