Simultaneous Spatial Panel Data Models with Common Shocks

76 Pages Posted: 8 Nov 2017 Last revised: 18 Mar 2022

See all articles by Lina Lu

Lina Lu

Federal Reserve Banks - Federal Reserve Bank of Boston

Date Written: August, 2017


I consider a simultaneous spatial panel data model, jointly modeling three effects: simultaneous effects, spatial effects and common shock effects. This joint modeling and consideration of cross-sectional heteroskedasticity result in a large number of incidental parameters. I propose two estimation approaches, a quasi-maximum likelihood (QML) method and an iterative generalized principal components (IGPC) method. I develop full inferential theories for the estimation approaches and study the trade-off between the model specifications and their respective asymptotic properties. I further investigate the finite sample performance of both methods using Monte Carlo simulations. I find that both methods perform well and that the simulation results corroborate the inferential theories. Some extensions of the model are considered. Finally, I apply the model to analyze the relationship between trade and GDP using a panel data over time and across countries.

Keywords: Panel data model, spatial models, Simultaneous equations system, Common shocks, Simultaneous effects, Incidental parameters, Maximum likelihood estimation, Principal components, High dimensionality, Inferential theory

JEL Classification: C13, C31, C33, C38, C51

Suggested Citation

Lu, Lina, Simultaneous Spatial Panel Data Models with Common Shocks (August, 2017). FRB Boston Risk and Policy Analysis Unit Paper No. RPA 17-3, Available at SSRN:

Lina Lu (Contact Author)

Federal Reserve Banks - Federal Reserve Bank of Boston ( email )

600 Atlantic Avenue
Boston, MA 02210
United States

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