Generalised spatial and spatiotemporal autoregressive conditional heteroscedasticity

authored by

Philipp Otto, Wolfgang Schmid

Abstract

In this paper, we introduce a new spatial model that incorporates heteroscedastic variance depending on neighboring locations. The proposed process is regarded as the spatial equivalent to the temporal autoregressive conditional heteroscedasticity (ARCH) model. We show additionally how the introduced spatial ARCH model can be used in spatiotemporal settings. In contrast to the temporal ARCH model, in which the distribution is known given the full information set of the prior periods, the distribution is not straightforward in the spatial and spatiotemporal setting. However, it is possible to estimate the parameters of the model using the maximum-likelihood approach. Via Monte Carlo simulations, we demonstrate the performance of the estimator for a specific spatial weighting matrix. Moreover, we combine the known spatial autoregressive model with the spatial ARCH model assuming heteroscedastic errors. Eventually, the proposed autoregressive process is illustrated using an empirical example. Specifically, we model lung cancer mortality in 3108 U. S. counties and compare the introduced model with two benchmark approaches.

External Organisation(s)

European University Viadrina in Frankfurt (Oder)

Type

Article

Journal

Spatial Statistics

Volume

26

Pages

125-145

No. of pages

21

ISSN

2211-6753

Publication date

02.09.2016

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Computers in Earth Sciences, Management, Monitoring, Policy and Law, Statistics and Probability

Sustainable Development Goals

SDG 3 - Good Health and Well-being

Electronic version(s)

https://doi.org/10.1016/j.spasta.2018.07.005 (Access: Unknown)