Directional spatial autoregressive dependence in the conditional first- and second-order moments

Merk, M.S. and Otto, P. (2021) Directional spatial autoregressive dependence in the conditional first- and second-order moments. Spatial Statistics, 41, 100490. (doi: 10.1016/j.spasta.2020.100490)

Abstract

In contrast to classical econometric approaches which are based on prespecified isotropic weighting schemes, we suggest that the spatial weighting matrix in the presence of directional dependencies should be estimated. We identify this direction based on different candidate neighbourhood sets. In this paper, we consider two different types of processes – namely spatial autoregressive and spatial autoregressive conditional heteroscedastic processes – and derive the consistency of the corresponding maximum likelihood estimates in the presence of directional dependencies. Moreover, Monte Carlo simulation results indicate that the model’s performance improves with sample size and with smaller neighbourhood subset sizes. Finally, we apply this approach to aerosol observations over the North Atlantic region and show that their spatial dependence matches the direction of the trade winds in this area.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Otto, Dr Philipp
• Authors: Merk, M.S., and Otto, P.
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Journal Name: Spatial Statistics
• Publisher: Elsevier
• ISSN: 2211-6753
• ISSN (Online): 2211-6753
• Published Online: 08 January 2021