Locally stationary spatio-temporal processes

Yasumasa Matsuda and Yoshihiro Yajima

No 72, DSSR Discussion Papers from Graduate School of Economics and Management, Tohoku University

Abstract: This paper proposes a locally stationary spatio-temporal processes to analyze the motivating example of US precipitation data, which is a huge data set composed of monthly observations of precipitation on thousands of monitoring points scattered irregularly all over US continent. Allowing the parameters of continuous autoregressive and moving average (CARMA) random fields by Brockwell and Matsuda [2] to be dependent spatially, we generalize locally stationary time series by Dahlhaus [3] to spatio-temporal processes that are locally stationary in space. We develop Whittle likelihood estimation for the spatially dependent parameters and derive the asymptotic properties rigorously. We demonstrate that the spatiotemporal models actually work to account for nonstationary spatial covariance structures in US precipitation data.

Pages: 14 pages
Date: 2018-01
New Economics Papers: this item is included in nep-ecm
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Persistent link: https://EconPapers.repec.org/RePEc:toh:dssraa:72

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