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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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

id	okr-10986-31331
recordtype	oai_dc
spelling	okr-10986-313312021-12-06T12:22:13Z Pollution and Expenditures in a Penalized Vector Spatial Autoregressive Time Series Model with Data-Driven Networks Andree, Bo Pieter Johannes Spencer, Phoebe Chamorro, Andres Wang, Dieter Azari, Sardar Feredun Dogo, Harun ENVIRONMENT POLLUTION POVERTY PENALIZED INFERENCE SPATIAL ANALYSIS IMPULSE RESPONSE VECTOR AUTOREGRESSION POLLUTION IMPACT URBAN AIR POLLUTION SVARMA MODEL 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 and estimation, focusing on a penalized Maximum Likelihood criterion. The empirical performance of the estimator is explored in a simulation study. The model is used to study a spatial time series of pollution and household expenditure data in Indonesia. The analysis finds that the new model improves in terms of implied density, and better neutralizes residual correlations than the VARMA, using fewer parameters. The results suggest that growth in household expenditures precedes pollution reduction, particularly after the expenditures of poorer households increase; that increasing pollution is followed by reduced growth in expenditures, particularly reducing the growth of poorer households; and that there are significant spillovers from bottom-up growth in expenditures. The paper does not find evidence for top-down growth spillovers. Feedback between the identified mechanisms may contribute to pollution-poverty traps and the results imply that pollution damages are economically significant. 2019-02-26T17:11:06Z 2019-02-26T17:11:06Z 2019-02 Working Paper 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 English Policy Research Working Paper;No. 8757 CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo World Bank World Bank, Washington, DC Publications & Research Publications & Research :: Policy Research Working Paper
repository_type	Digital Repository
institution_category	Foreign Institution
institution	Digital Repositories
building	World Bank Open Knowledge Repository
collection	World Bank
language	English
topic	ENVIRONMENT POLLUTION POVERTY PENALIZED INFERENCE SPATIAL ANALYSIS IMPULSE RESPONSE VECTOR AUTOREGRESSION POLLUTION IMPACT URBAN AIR POLLUTION SVARMA MODEL
spellingShingle	ENVIRONMENT POLLUTION POVERTY PENALIZED INFERENCE SPATIAL ANALYSIS IMPULSE RESPONSE VECTOR AUTOREGRESSION POLLUTION IMPACT URBAN AIR POLLUTION SVARMA MODEL Andree, Bo Pieter Johannes Spencer, Phoebe Chamorro, Andres Wang, Dieter Azari, Sardar Feredun Dogo, Harun Pollution and Expenditures in a Penalized Vector Spatial Autoregressive Time Series Model with Data-Driven Networks
relation	Policy Research Working Paper;No. 8757
description	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 and estimation, focusing on a penalized Maximum Likelihood criterion. The empirical performance of the estimator is explored in a simulation study. The model is used to study a spatial time series of pollution and household expenditure data in Indonesia. The analysis finds that the new model improves in terms of implied density, and better neutralizes residual correlations than the VARMA, using fewer parameters. The results suggest that growth in household expenditures precedes pollution reduction, particularly after the expenditures of poorer households increase; that increasing pollution is followed by reduced growth in expenditures, particularly reducing the growth of poorer households; and that there are significant spillovers from bottom-up growth in expenditures. The paper does not find evidence for top-down growth spillovers. Feedback between the identified mechanisms may contribute to pollution-poverty traps and the results imply that pollution damages are economically significant.
format	Working Paper
author	Andree, Bo Pieter Johannes Spencer, Phoebe Chamorro, Andres Wang, Dieter Azari, Sardar Feredun Dogo, Harun
author_facet	Andree, Bo Pieter Johannes Spencer, Phoebe Chamorro, Andres Wang, Dieter Azari, Sardar Feredun Dogo, Harun
author_sort	Andree, Bo Pieter Johannes
title	Pollution and Expenditures in a Penalized Vector Spatial Autoregressive Time Series Model with Data-Driven Networks
title_short	Pollution and Expenditures in a Penalized Vector Spatial Autoregressive Time Series Model with Data-Driven Networks
title_full	Pollution and Expenditures in a Penalized Vector Spatial Autoregressive Time Series Model with Data-Driven Networks
title_fullStr	Pollution and Expenditures in a Penalized Vector Spatial Autoregressive Time Series Model with Data-Driven Networks
title_full_unstemmed	Pollution and Expenditures in a Penalized Vector Spatial Autoregressive Time Series Model with Data-Driven Networks
title_sort	pollution and expenditures in a penalized vector spatial autoregressive time series model with data-driven networks
publisher	World Bank, Washington, DC
publishDate	2019
url	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
_version_	1764474083154591744

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

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