Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/198902 
Year of Publication: 
2019
Series/Report no.: 
CESifo Working Paper No. 7542
Publisher: 
Center for Economic Studies and ifo Institute (CESifo), Munich
Abstract: 
This paper considers the problem of identification, estimation and inference in the case of spatial panel data models with heterogeneous spatial lag coefficients, with and without (weakly) exogenous regressors, and subject to heteroskedastic errors. A quasi maximum likelihood (QML) estimation procedure is developed and the conditions for identification of spatial coefficients are derived. Regularity conditions are established for the QML estimators of individual spatial coefficients, as well as their means (the mean group estimators), to be consistent and asymptotically normal. Small sample properties of the proposed estimators are investigated by Monte Carlo simulations for Gaussian and non-Gaussian errors, and with spatial weight matrices of differing degrees of sparsity. The simulation results are in line with the paper's key theoretical findings even for panels with moderate time dimensions, irrespective of the number of cross section units. An empirical application to U.S. house price changes during the 1975-2014 period shows a significant degree of heterogeneity in spill-over effects over the 338 Metropolitan Statistical Areas considered.
Subjects: 
spatial panel data models
heterogeneous spatial lag coefficients
identification
quasi maximum likelihood (QML) estimators
non-Gaussian errors
house price changes
Metropolitan Statistical Areas
JEL: 
C21
C23
Document Type: 
Working Paper
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