Pollution and Expenditures in a Penalized Vector Spatial Autoregressive Time Series Model with Data-Driven Networks

Pollution and Expenditures in a Penalized Vector Spatial Autoregressive Time Series Model with Data-Driven Networks

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 speci...

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Bibliographic Details
Main Authors: Andree, Bo Pieter Johannes, Spencer, Phoebe, Chamorro, Andres, Wang, Dieter, Azari, Sardar Feredun, Dogo, Harun
Format: Working Paper
Language:English
Published: World Bank, Washington, DC 2019
Subjects:
ENVIRONMENT
POLLUTION
POVERTY
PENALIZED INFERENCE
SPATIAL ANALYSIS
IMPULSE RESPONSE
VECTOR AUTOREGRESSION
POLLUTION IMPACT
URBAN AIR POLLUTION
SVARMA MODEL
Online Access:http://documents.worldbank.org/curated/en/162631551119359071/Pollution-and-Expenditures-in-a-Penalized-Vector-Spatial-Autoregressive-Time-Series-Model-with-Data-Driven-Networks
http://hdl.handle.net/10986/31331

Internet

http://documents.worldbank.org/curated/en/162631551119359071/Pollution-and-Expenditures-in-a-Penalized-Vector-Spatial-Autoregressive-Time-Series-Model-with-Data-Driven-Networks
http://hdl.handle.net/10986/31331

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