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Spatial Autoregressive Conditional Heteroscedasticity Model and Its Application

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  • Takaki Sato
  • Yasumasa Matsuda

Abstract

This paper proposes spatial autoregressive conditional heteroscedasticity (S- ARCH) models to estimate spatial volatility in spatial data. S-ARCH model is a spatial extension of time series ARCH model. S-ARCH models specify conditional variances as the variances given the values of surrounding observations in spatial data, which is regarded as a spatial extension of time series ARCH models that spec- ify conditional variances as the variances given the values of past observations. We consider parameter estimation for S-ARCH models by maximum likelihood method and propose test statistics for ARCH effects in spatial data. We demonstrate the empirical properties by simulation studies and real data analysis of land price data in Tokyo.

Suggested Citation

  • Takaki Sato & Yasumasa Matsuda, 2016. "Spatial Autoregressive Conditional Heteroscedasticity Model and Its Application," TERG Discussion Papers 348, Graduate School of Economics and Management, Tohoku University.
    • Handle: RePEc:toh:tergaa:348
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    File URL: http://hdl.handle.net/10097/63819
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    References listed on IDEAS

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    5. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
    6. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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