Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/171820
Authors: 
Ahrens, Achim
Bhattacharjee, Arnab
Year of Publication: 
2015
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 3 [Year:] 2015 [Issue:] 1 [Pages:] 128-155
Abstract: 
The vast majority of spatial econometric research relies on the assumption that the spatial network structure is known a priori. This study considers a two-step estimation strategy for estimating the n(n..1) interaction effects in a spatial autoregressive panel model where the spatial dimension is potentially large. The identifying assumption is approximate sparsity of the spatial weights matrix. The proposed estimation methodology exploits the Lasso estimator and mimics two-stage least squares (2SLS) to account for endogeneity of the spatial lag. The developed two-step estimator is of more general interest. It may be used in applications where the number of endogenous regressors and the number of instrumental variables is larger than the number of observations. We derive convergence rates for the two-step Lasso estimator. Our Monte Carlo simulation results show that the two-step estimator is consistent and successfully recovers the spatial network structure for reasonable sample size, T.
Subjects: 
lasso
endogeneity
unkown W
spatial weights matrix
JEL: 
C23
C33
C52
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/4.0/
Document Type: 
Article

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