We propose generalized moments LASSO estimator, combining LASSO with GMM, for penalized variable selection and estimation under the spatial error model with spatially autoregressive errors. We establish parameter consistency and selection sign consistency of the proposed estimator in the low dimensional setting when the parameter dimension p < sample size n , as well as the high dimensional setting with p greater than and growing with n. Finite sample performance of the method is examined by simulation, compared against the LASSO for IID data. The methods are applied to estimation of a spatial Durbin model for the Aveiro housing market (Portugal).
|Number of pages||55|
|Early online date||6 Nov 2018|
|Publication status||Published - Sept 2019|
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- School of Social Sciences, Edinburgh Business School - Professor
- School of Social Sciences - Professor
Person: Academic (Research & Teaching)