Dynamic spatiotemporal ARCH models

Philipp Otto, Osman Doğan and Süleyman Taşpınar

Spatial Economic Analysis, 2024, vol. 19, issue 2, 250-271

Abstract: Geo-referenced data are characterised by an inherent spatial dependence due to geographical proximity. In this paper, we introduce a dynamic spatiotemporal autoregressive conditional heteroscedasticity (ARCH) process to describe the effects of (i) the log-squared time-lagged outcome variable, the temporal effect, (ii) the spatial lag of the log-squared outcome variable, the spatial effect, and (iii) the spatiotemporal effect on the volatility of an outcome variable. We derive a generalised method of moments (GMM) estimator based on the linear and quadratic moment conditions. We show the consistency and asymptotic normality of the GMM estimator. After studying the finite-sample performance in simulations, the model is demonstrated by analysing monthly log-returns of condominium prices in Berlin from 1995 to 2015, for which we found significant volatility spillovers.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/17421772.2023.2254817 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:specan:v:19:y:2024:i:2:p:250-271

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RSEA20

DOI: 10.1080/17421772.2023.2254817

Access Statistics for this article

Spatial Economic Analysis is currently edited by Bernie Fingleton and Danilo Igliori

More articles in Spatial Economic Analysis from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().