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

57 Pages Posted: 26 Feb 2019 Last revised: 27 Feb 2019

See all articles by Bo Pieter Johannes Andree

Bo Pieter Johannes Andree

Vrije Universiteit Amsterdam, School of Business and Economics; World Bank; Tinbergen Institute

Phoebe Girouard Spencer

World Bank

Sardar Azari

World Bank

Andres Chamorro

World Bank

Dieter Wang

World Bank

Harun Dogo

World Bank

Date Written: February 25, 2019

Abstract

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.

Keywords: Global Environment, Inequality, Brown Issues and Health, Air Quality & Clean Air, Pollution Management & Control, Health Service Management and Delivery

Suggested Citation

Andree, Bo Pieter Johannes and Spencer, Phoebe Girouard and Azari, Sardar and Chamorro, Andres and Wang, Dieter and Dogo, Harun, Pollution and Expenditures in a Penalized Vector Spatial Autoregressive Time Series Model with Data-Driven Networks (February 25, 2019). World Bank Policy Research Working Paper No. 8757, Available at SSRN: https://ssrn.com/abstract=3341739

Bo Pieter Johannes Andree (Contact Author)

Vrije Universiteit Amsterdam, School of Business and Economics ( email )

De Boelelaan 1105
Amsterdam, 1081HV
Netherlands

World Bank ( email )

1818 H Street, NW
Washington, DC 20433
United States

Tinbergen Institute ( email )

Burg. Oudlaan 50
Rotterdam, 3062 PA
Netherlands

Phoebe Girouard Spencer

World Bank

1818 H Street, NW
Washington, DC 20433
United States

Sardar Azari

World Bank

1818 H Street, NW
Washington, DC 20433
United States

Andres Chamorro

World Bank

1818 H Street, NW
Washington, DC 20433
United States

Dieter Wang

World Bank ( email )

1818 H Street, NW
Washington, DC 20433
United States

Harun Dogo

World Bank ( email )

1818 H Street, NW
Washington, DC 20433
United States

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