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A Scalable MCEM Estimator for Spatio-Temporal Autoregressive Models
Discussion paper / Internet publication
 
ID 4493238
Author(s) Hunziker, Philipp; Wucherpfennig, Julian; Kachi, Aya; Bormann, Nils-Christian
Author(s) at UniBasel Kachi, Aya
Year 2018
Month and day 07-18
Title A Scalable MCEM Estimator for Spatio-Temporal Autoregressive Models
Pages 29
Publisher / Institution arXiv
URL https://arxiv.org/abs/1807.07133
Keywords spatial econometrics; spatial regression; political methodology; statistical methodology; big data; mcem; count data; discrete choice
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.
edoc-URL https://edoc.unibas.ch/67974/
Full Text on edoc No
Top-publication of... Kachi, Aya
 
   

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