Parametric bootstrap inferences for unbalanced panel data models

ABSTRACT

This article presents parametric bootstrap (PB) approaches for hypothesis testing and interval estimation for the regression coefficients of panel data regression models with incomplete panels. Some simulation results are presented to compare the performance of the PB approaches with the approximate inferences. Our studies show that the PB approaches perform satisfactorily for various sample sizes and parameter configurations, and the performance of PB approaches is mostly better than the approximate methods with respect to the coverage probabilities and the Type I error rates. The PB inferences have almost exact coverage probabilities and Type I error rates. Furthermore, the PB procedure can be simply carried out by a few simulation steps, and the derivation is easier to understand and to be extended to the multi-way error component regression models with unbalanced panels. Finally, the proposed approaches are illustrated by using a real data example.

KEYWORDS:

• Bootstrap resampling
• Coverage probability
• Missing data
• Parametric bootstrap pivotal variable

MATHEMATICS SUBJECT CLASSIFICATION:

• Primary 62F40
• Secondary 62J05

Funding

This work was supported by the National Natural Science Foundation of China (11171002), the China Postdoctoral Science Foundation (2016M600157) and the Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions (CIT&TCD201404002) and Beijing Natural Science Foundation.