Two-Step Lasso Estimation of the Spatial Weights Matrix

Econometrics

Achim Ahrens¹  Arnab Bhattacharjee² 

[1] Spatial Economics and Econometrics Centre (SEEC), Heriot-Watt University, Edinburgh, Scotland EH14 4AS, UK; E-Mail

关键词: lasso;    endogeneity;    unkown W;    spatial weights matrix;   

DOI  :  10.3390/econometrics3010128

来源: mdpi

【 摘 要 】

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 theinteraction 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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.