Spatial extension of generalized autoregressive conditional heteroskedasticity models

Takaki Sato, Yasumasa Matsuda

研究成果: Article査読

4 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)148-160
ページ数13
ジャーナルSpatial Economic Analysis
16
2
DOI
出版ステータスPublished - 2021

ASJC Scopus subject areas

  • 地理、計画および開発
  • 経済学、計量経済学および金融学(全般)
  • 統計学、確率および不確実性
  • 地球惑星科学(その他)

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