Spatial extension of generalized autoregressive conditional heteroskedasticity models

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2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)148-160
Number of pages13
JournalSpatial Economic Analysis
Volume16
Issue number2
DOIs
Publication statusPublished - 2021

Keywords

  • C31
  • C51
  • C58
  • GARCH model
  • R30
  • quasi-maximum likelihood
  • spatial ARMA model
  • spatial volatility

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Economics, Econometrics and Finance(all)
  • Statistics, Probability and Uncertainty
  • Earth and Planetary Sciences (miscellaneous)

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