Spatial extension of generalized autoregressive conditional heteroskedasticity models

Takaki Sato and Yasumasa Matsuda

Spatial Economic Analysis, 2021, vol. 16, issue 2, 148-160

Abstract: This paper proposes an extension of generalized autoregressive conditional heteroskedasticity (GARCH) models for a time series to those for spatial data, which are called here spatial GARCH (S-GARCH) models. S-GARCH models are re-expressed as spatial autoregressive moving-average (SARMA) models and a two-step procedure based on quasi-likelihood functions is proposed to estimate the parameters. The consistency and asymptotic normality are proven for the two-step estimators. S-GARCH models are applied to simulated and land-price data in areas of Tokyo to demonstrate the empirical properties.

Date: 2021
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DOI: 10.1080/17421772.2020.1742929

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