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A Scalable MCEM Estimator for Spatio-Temporal Autoregressive Models

Hunziker, Philipp and Wucherpfennig, Julian and Kachi, Aya and Bormann, Nils-Christian. (2018) A Scalable MCEM Estimator for Spatio-Temporal Autoregressive Models.

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Official URL: https://edoc.unibas.ch/67974/

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Abstract

Very large spatio-temporal lattice data are becoming increasingly common across a variety of disciplines. However, estimating interdependence across space and time in large areal datasets remains challenging, as existing approaches are often (i) not scalable, (ii) designed for conditionally Gaussian outcome data, or (iii) are limited to cross-sectional and univariate outcomes. This paper proposes an MCEM estimation strategy for a family of latent-Gaussian multivariate spatio-temporal models that addresses these issues. The proposed estimator is applicable to a wide range of non-Gaussian outcomes, and implementations for binary and count outcomes are discussed explicitly. The methodology is illustrated on simulated data, as well as on weekly data of IS-related events in Syrian districts.
Faculties and Departments:06 Faculty of Business and Economics > Departement Wirtschaftswissenschaften > Professuren Wirtschaftswissenschaften > Political Economics of Energy Policy (Kachi)
UniBasel Contributors:Kachi, Aya
Item Type:Working Paper
Publisher:arXiv
Number of Pages:29
Note:Publication type according to Uni Basel Research Database: Discussion paper / Internet publication
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Last Modified:19 Feb 2019 15:53
Deposited On:19 Feb 2019 15:53

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