This paper introduces a Spatial Vector Autoregressive Moving Average (SVARMA) model in which multiple cross-sectional time series are modeled as multivariate, possibly fat-tailed, spatial autoregressive ARMA processes. The estimation requires specifying the cross-sectional spillover channels through spatial weights matrices. the paper explores a kernel method to estimate the network topology based on similarities in the data. It discusses the model...
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INFORMACIÓN
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2019/02/25
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Documento de trabajo sobre investigaciones relativas a políticas
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WPS8757
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1
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1
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2019/02/25
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Disclosed
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Pollution and Expenditures in a Penalized Vector Spatial Autoregressive Time Series Model with Data-Driven Networks
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Environment and Natural Resources