Estimation of the spatial weighting matrix for spatiotemporal data under the presence of structural breaks

Otto, P. and Steinert, R. (2023) Estimation of the spatial weighting matrix for spatiotemporal data under the presence of structural breaks. Journal of Computational and Graphical Statistics, 32(2), pp. 696-711. (doi: 10.1080/10618600.2022.2107530)

Abstract

In this article, we propose a two-stage LASSO estimation approach for the estimation of a full spatial weight matrix of spatiotemporal autoregressive models. In addition, we allow for an unknown number of structural breaks in the local means of each spatial location. These locally varying mean levels, however, can easily be mistaken as spatial dependence and vice versa. Thus, the proposed approach jointly estimates the spatial dependence, all structural breaks, and the local mean levels. For selection of the penalty parameter, we propose a completely new selection criterion based on the distance between the empirical spatial autocorrelation and the spatial dependence estimated in the model. Through simulation studies, we will show the finite-sample performance of the estimators and provide practical guidance as to when the approach could be applied. Finally, the method will be illustrated by an empirical example of intra-city monthly real-estate prices in Berlin between 1995 and 2014. The spatial units will be defined by the respective postal codes. The new approach allows us to estimate local mean levels and quantify the deviation of the observed prices from these levels due to spatial spillover effects. In doing so, the entire spatial dependence structure is estimated on a data-driven basis. Supplementary materials for this article are available online.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Otto, Dr Philipp
• Authors: Otto, P., and Steinert, R.
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Journal Name: Journal of Computational and Graphical Statistics
• Publisher: Taylor & Francis
• ISSN: 1061-8600
• ISSN (Online): 1537-2715
• Published Online: 04 October 2022
• Copyright Holders: Copyright © 2022 American Statistical Association and Institute of Mathematical Statistics
• First Published: First published in Journal of Computational and Graphical Statistics 32(2):696-711
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher

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