Two-Step Lasso Estimation of the Spatial Weights Matrix
By A. Ahrens, Arnab Bhattacharjee. 2015
69 citations recorded by the source corpus.
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 .
Subfields: Economics and Econometrics
View Paper: https://www.semanticscholar.org/paper/ef4b01eb9e18c648633210ab55f4099a8f042c1b
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In Economics and Econometrics
Citations in the corpus
- Functional-coefficient spatial autoregressive models with nonparametric spatial weights (2016, methodology): “Ahrens and Bhattacharjee (2015) propose to estimate each spatial weights (wij) via the LASSO method under certain matrix sparsity condition for spatiotemporal data, when the number of units is much smaller than the number of time periods.”
- Estimation and Selection of Spatial Weight Matrix in a Spatial Lag Model (2019, methodology): “In this sense, the sparsity assumption for the adjustment matrix is more easily satisfied than those in Ahrens and Bhattacharjee (2015) or Lam and Souza (2015), which require the spatial weight matrix itself to be sparse.”