Testing for parametric component of partially linear models with missing covariates

This paper considers the testing problem of partially linear models with missing covariates. The inverse probability weighted restricted estimator for the parametric component under linear constraint is derived and proven to share asymptotically normal distribution. To test the linear constraint, we...

Full description

Saved in:
Bibliographic Details
Published in:Statistical papers (Berlin, Germany) Germany), 2019-06, Vol.60 (3), p.747-760
Main Authors: Zhou, Zhangong, Tang, Linjun
Format: Article
Language:English
Subjects:
Computer simulation
Data analysis
Economic Theory/Quantitative Economics/Mathematical Methods
Economics
Empirical analysis
Estimating techniques
Finance
Insurance
Lagrange multiplier
Management
Mathematics and Statistics
Normal distribution
Null hypothesis
Operations Research/Decision Theory
Probability Theory and Stochastic Processes
Regression analysis
Regular Article
Statistical analysis
Statistical tests
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:This paper considers the testing problem of partially linear models with missing covariates. The inverse probability weighted restricted estimator for the parametric component under linear constraint is derived and proven to share asymptotically normal distribution. To test the linear constraint, we construct two test statistics based on the the Lagrange multiplier and the empirical likelihood methods. The limiting distributions of the resulting test statistics are both standard chi-squared distributions under the null hypothesis. Simulation studies and a real data analysis are conducted to illustrate relevant performances.
ISSN:0932-5026
1613-9798
DOI:10.1007/s00362-016-0848-6