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Estimation and selection of spatial weight matrix in a spatial lag model

Lam, Clifford and Souza, Pedro C.L. (2019) Estimation and selection of spatial weight matrix in a spatial lag model. Journal of Business and Economic Statistics. ISSN 0735-0015 (In Press)

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Pending embargo until 1 January 2100.

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[img] Text
Pending embargo until 1 January 2100.

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Abstract

Spatial econometric models allow for interactions among variables through the specification of a spatial weight matrix. Practitioners often face the risk of misspecification of such a matrix. In many problems a number of potential specifications exist, such as geographic distances, or various economic quantities among variables. We propose estimating the best linear combination of these specifications, added with a potentially sparse adjustment matrix. The coefficients in the linear combination, together with the sparse adjustment matrix, are subjected to variable selection through the adaptive Least Absolute Shrinkage and Selection Operator (LASSO). As a special case, if no spatial weight matrices are specified, the sparse adjustment matrix becomes a sparse spatial weight matrix estimator of our model. Our method can therefore be seen as a unified framework for the estimation and selection of a spatial weight matrix. The rate of convergence of all proposed estimators are determined when the number of time series variables can grow faster than the number of time points for data, while Oracle properties for all penalized estimators are presented. Simulations and an application to stocks data confirms the good performance of our procedure.

Item Type: Article
Official URL: https://www.tandfonline.com/toc/ubes20/current
Additional Information: © 2019 Informa UK Limited
Divisions: Statistics
Subjects: Q Science > QA Mathematics
Sets: Departments > Statistics
Date Deposited: 04 Jan 2019 10:55
Last Modified: 14 Feb 2019 00:05
URI: http://eprints.lse.ac.uk/id/eprint/91501

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