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Two-Step Lasso Estimation of the Spatial Weights Matrix

Two-Step Lasso Estimation of the Spatial Weights Matrix

Authors :
Achim Ahrens
Arnab Bhattacharjee
Source :
Econometrics, Vol 3, Iss 1, Pp 128-155 (2015)
Publication Year :
2015
Publisher :
MDPI AG, 2015.

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.

Details

Language :
English
ISSN :
22251146
Volume :
3
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Econometrics
Publication Type :
Academic Journal
Accession number :
edsdoj.bf64445c65564053b424a43bc436bbe1
Document Type :
article
Full Text :
https://doi.org/10.3390/econometrics3010128