Robust estimation method for panel interval-valued data model with fixed effects

Abstract

Panel data model with fixed effects is widely used in economic and administrative applications. However, the presence of factors: measurement errors, data variability and outliers may potentially decrease the accuracy of the model prediction. In this paper, we use panel interval-valued data to represent measurement errors and data volatility of observations. Further, we propose a corresponding panel interval-valued data model with fixed effects, in which both the response and explanatory variables are interval-valued data. To reduce the impact of outliers on our model, we propose a robust estimation method based on the iterative weighted least squares technique. Later, Monte Carlo simulation and empirical application demonstrate that our model is a suitable tool for analyzing the behaviour of panel interval-valued data.

Keywords:

• Panel interval-valued data
• robust estimation
• iterative algorithm
• panel interval Box–Cox transformation

Mathematics Subject Classifications:

• 62H12
• 62J05
• 62-08

Acknowledgments

The authors would like to thank the associate editor and the reviewers for their useful feedback that improved this paper.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Funding

This paper was supported by the Natural Science Foundation of Hebei Province (No. A2022201002).