Dynamic spatiotemporal ARCH models

Philipp Otto*, Osman Doğan, Süleyman Taşpınar

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3 Atıf (Scopus)

Özet

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.

Orijinal dilİngilizce
Sayfa (başlangıç-bitiş)250-271
Sayfa sayısı22
DergiSpatial Economic Analysis
Hacim19
Basın numarası2
DOI'lar
Yayın durumuYayınlandı - 2024

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© 2023 Regional Studies Association.

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