Description/Abstract
This paper derives a feasible GLS estimator for a two-way error component model with serial correlation on both the time effects as well as the remainder disturbances. This estimator is based on two methods, one proposed by De Porres and Krishnakumar (2013) for deriving the spectral decomposition of a general error component structure and the other based on an inversion trick for the variance-covariance matrix of this model suggested by Skoglund and Karlsson (2001). While the last paper used maximum likelihood methods under the normality assumption, we use method of moments estimators following Baltagi and Li (1991) for the one-way error component model with serially correlated remainder disturbances and its extension by Brou et al. (2011) for the two-way model with serially correlated time effects as well as remainder disturbances. Monte Carlo simulations are performed to compare the performance of these two estimators as well as a bias correction version based on Nobach (2023). Our results find that the method based on the Skoglund and Karlsson (2001) inverse that is bias corrected a la Nobach (2023) performs the best in root mean square error (RMSE) as well as mean absolute percentage error (MAPE) and is recommended.
Document Type
Working Paper
Date
5-15-2025
Keywords
Two-way random effects, two-way fixed effects, serial correlation, panel data, feasible Generalized Least Squares
Language
English
Series
Working Papers Series
Acknowledgements
This paper is written in honor of Robin Sickles for his many contributions to econometrics, in particular in particular panel data. We would like to thank the editor Subal C. Kumbhakar and two anonymous referees for their valuable comments and suggestions.
Disciplines
Economic Policy | Economics
ISSN
1525-3066
Recommended Citation
Baltagi, Badi H.; Bresson, Georges; and Etienne, Jean-Michel, "Two-Way Random Effects Model with Serial Correlation" (2025). Center for Policy Research. 501.
https://surface.syr.edu/cpr/501
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
