Spatiotemporal Event Studies for Environmental Data Under Cross-Sectional Dependence: An Application to Air Quality Assessment in Lombardy

Paolo Maranzano () and Matteo Pelagatti
Additional contact information
Paolo Maranzano: University of Milano-Bicocca

Journal of Agricultural, Biological and Environmental Statistics, 2024, vol. 29, issue 1, No 9, 147-168

Abstract: Abstract We propose a twofold adjustment for Event Studies considering spatiotemporal data in a multivariate time series framework where the data are characterized by spatial and temporal dependence. The first adjustment consists of modeling the spatiotemporal dynamics of the data by implementing several geostatistical models capable of handling both spatial and temporal components, as well as estimating the relationship between the response variable and a set of exogenous factors. With the second adjustment, we propose to use cross-sectional-adjusted test statistics directly accounting for spatial cross-correlation. The proposed methods are applied to the case of NO $$_2$$ 2 concentrations observed in Northern Italy during the first wave of the COVID-19 pandemic. The key findings are as follows. First, all the considered geostatistical models estimate larger reductions in the major metropolitan and congested areas, while smaller reductions are estimated in rural plains and in the mountains. Second, the models are nearly equivalent in terms of fitting and are capable of identifying the true event window. Third, by using spatiotemporal models we ensure the residuals are uncorrelated across space and time, thus allowing Event Studies test statistics to provide reliable and realistic estimates. Fourth, as expected, all test statistics show significant reductions in NO $$_2$$ 2 concentrations starting from the first few days of lockdown. Supplementary materials accompanying this paper appear online.

Keywords: Multivariate time series; Spatial cross-sectional dependence; Event Studies; Air quality; Abnormal concentrations; C12; C23; C32; G14; Q53; Q58 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13253-023-00564-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jagbes:v:29:y:2024:i:1:d:10.1007_s13253-023-00564-z

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/13253

DOI: 10.1007/s13253-023-00564-z

Access Statistics for this article

Journal of Agricultural, Biological and Environmental Statistics is currently edited by Stephen Buckland

More articles in Journal of Agricultural, Biological and Environmental Statistics from Springer, The International Biometric Society, American Statistical Association
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().