This study investigates spatial panel data models with a multifactor error structure and multiple structural breaks occurring in the coefficients of both spatial lagged and explanatory variables. While extensive research has addressed cross-sectional dependence in panel data, including approaches that integrate spatial and factor structures within a single framework, few studies account for time-varying model parameters and achieving consistent estimation remains a significant challenge. To address the dual challenges of endogeneity and time heterogeneity, we propose a novel penalized generalized method of moments estimation with common correlated effects (PGMM-CCEX). Specifically, this method addresses the endogeneity issue by utilizing the cross-sectional averages of regressors as factor proxies when constructing the internal instrumental variables, while employing adaptive group fused Lasso to detect multiple structural breaks. The PGMM-CCEX method consistently estimates both the number of breaks and their locations. Furthermore, the post-PGMM-CCEX regime-specific coefficient estimates are consistent and asymptotically follow a normal distribution. Notably, the method remains valid even when factor loadings vary over time, whether synchronously or asynchronously with the parameters of interest. Monte Carlo simulations confirm the satisfactory finite-sample performance of the proposed PGMM-CCEX method. Finally, we apply our method to analyze cross-country economic growth across 106 countries from 1970 to 2019, revealing the time-varying influence of key economic factors on growth dynamics.

Publication:

JOURNAL OF ECONOMETRICS

http://dx.doi.org/10.1016/j.jeconom.2025.106082

Author:

Siqi Dai

College of Finance and Statistics, Hunan University, Changsha, 410006, China

E-mail addresses: daisiqi9933@hnu.edu.cn

Yongmiao Hong

National Laboratory of Mathematical Science and Center for Forecasting Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China

School of Economics and Management and MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation, University of Chinese Academy of Sciences, Beijing, 100190, China

E-mail addresses: yh20@cornell.edu

Haiqi Li

College of Finance and Statistics, Hunan University, Changsha, 410006, China

Corresponding author.

E-mail addresses: lihaiqi00@hnu.edu.cn

Chaowen Zheng

Department of Economics, University of Southampton, Southampton, SO17 1BJ, UK

E-mail addresses: chaowen.zheng@soton.ac.uk