Two-Step Lasso Estimation of the Spatial Weights Matrix

Achim Ahrens, Arnab Bhattacharjee

Research output: Contribution to journalArticle

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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. 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. We also present Monte Carlo simulation results which show that the two-step estimator is consistent and successfully recovers the spatial network structure for reasonable large sample size, T.
Original languageEnglish
Pages (from-to)128-155
Number of pages28
JournalEconometrics
Volume3
Issue number1
DOIs
Publication statusPublished - 9 Mar 2015
EventIASSL Conference on "Statistics and Society in the New Information Age: Challenges and Opportunities" - Colombo, Sri Lanka
Duration: 28 Dec 201430 Dec 2014

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