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Federal Reserve Bank of Boston
Supervisory Research and Analysis Working Papers
Simultaneous Spatial Panel Data Models with Common Shocks
Lina Lu
Abstract

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.


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Lina Lu, Simultaneous Spatial Panel Data Models with Common Shocks, Federal Reserve Bank of Boston, Supervisory Research and Analysis Working Papers RPA 17-3, 09 Aug 2017.
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Keywords: Panel data model; Spatial model; Simultaneous equations system; Common shocks; Simultaneous effects; Incidental parameters; Maximum likelihood estimation; Principal components; High dimensionality; Inferential theory
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