Spatial Autoregressive Conditional Heteroscedasticity Model and Its Application

Takaki Sato and Yasumasa Matsuda

No 348, TERG Discussion Papers from Graduate School of Economics and Management, Tohoku University

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.

Pages: 16 pages
Date: 2016-04-26
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-geo and nep-ure
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http://hdl.handle.net/10097/63819

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Persistent link: https://EconPapers.repec.org/RePEc:toh:tergaa:348

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