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...
| Main Authors: | , , , , , |
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| Format: | Working Paper |
| Language: | English |
| Published: |
World Bank, Washington, DC
2019
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| Subjects: | |
| 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 |
| Summary: | 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. |
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