Spatial Autoregressive Conditional Heteroskedasticity Models

DOI10.14490/jjss.47.221zbMath1395.62281OpenAlexW2805542278MaRDI QIDQ4578224

Yasumasa Matsuda, Takaki Sato

Publication date: 8 August 2018

Published in: JOURNAL OF THE JAPAN STATISTICAL SOCIETY (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.14490/jjss.47.221

zbMATH Keywords

Markov random fieldARCH modelareal dataspatial weight matrixspatial volatility

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Inference from spatial processes (62M30) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items (3)

A general framework for spatial GARCH models ⋮ Bayesian estimation and model selection for the spatiotemporal autoregressive model with autoregressive conditional heteroscedasticity errors ⋮ A Stationary Spatio‐Temporal GARCH Model

Uses Software

• Arc_Mat
• spBayes

Cites Work

• A test for spatial autocorrelation in seemingly unrelated regressions
• Time series: theory and methods.
• Generalized autoregressive conditional heteroscedasticity
• Nonlinear time series. Nonparametric and parametric methods
• Large-sample inference on spatial dependence
• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
• Estimation Methods for Models of Spatial Interaction
• Asymptotic Distributions of Quasi-Maximum Likelihood Estimators for Spatial Autoregressive Models
• A test for the serial independence of residuals
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