Paolo Maranzano, Matteo Maria Pelagatti
arXiv 24 Oct 2022 · Statistics — Applications
arXiv:2210.17529 · PDF · DOI · OpenAlex · Extracted main text
Event Studies (ES) are statistical tools that assess whether a particular event of interest has caused changes in the level of one or more relevant time series. We are interested in ES applied to multivariate time series characterized by high spatial (cross-sectional) and temporal dependence. We pursue two goals. First, we propose to extend the existing taxonomy on ES, mainly deriving from the financial field, by generalizing the underlying statistical concepts and then adapting them to the time series analysis of airborne pollutant concentrations. Second, we address the spatial cross-sectional dependence by adopting a twofold adjustment. Initially, we use a linear mixed spatio-temporal regression model (HDGM) to estimate the relationship between the response variable and a set of exogenous factors, while accounting for the spatio-temporal dynamics of the observations. Later, we apply a set of sixteen ES test statistics, both parametric and nonparametric, some of which directly adjusted for cross-sectional dependence. We apply ES to evaluate the impact on NO2 concentrations generated by the lockdown restrictions adopted in the Lombardy region (Italy) during the COVID-19 pandemic in 2020. The HDGM model distinctly reveals the level shift caused by the event of interest, while reducing the volatility and isolating the spatial dependence of the data. Moreover, all the test statistics unanimously suggest that the lockdown restrictions generated significant reductions in the average NO2 concentrations.
appendix boundary found by appendix_titled_section at “Supplementary materials” · 75% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Fassò, A., Maranzano, P., and Otto, P (2021) Spatiotemporal variable selection and air quality impact assessment of covid-19 lockdown self | 0.928 | 4 | 3 | 100% |
| 2 | Pelagatti, M. and Maranzano, P (2021) Nonparametric tests for event studies under cross-sectional dependence self | 0.874 | 7 | 2 | 100% |
| 3 | Calculli, C., Fassò, A., Finazzi, F., Pollice, A., and Turnone, A (2015) Maximum likelihood estimation of the multivariate hidden dynamic geostatistical model with application to air quality in apulia,… | 0.843 | 3 | 3 | 100% |
| 4 | Kolari, J. W. and Pynnönen, S (2011) Nonparametric rank tests for event studies | 0.693 | 6 | 1 | 100% |
| 5 | Ferreira, G., Mateu, J., and Porcu, E (2022) Multivariate kalman filtering for spatio-temporal processes | 0.644 | 2 | 2 | 100% |
| 6 | Brown, S. and Warner, J (1985) Using daily stock returns: the case of event studies | 0.585 | 3 | 1 | 100% |
| 7 | Abraham, B (1980) Intervention analysis and multiple time series | 0.511 | 2 | 1 | 100% |
| 8 | Corrado, C. J (1989) A nonparametric test for abnormal security-price performance in event studies | 0.511 | 2 | 1 | 100% |
| 9 | Corrado, C. J. and Zivney, T. L (1992) The specification and power of the sign test in event study hypothesis tests using daily stock returns | 0.511 | 2 | 1 | 100% |
| 10 | Wang, Y., Finazzi, F., and Fassò, A (2021) D-stem v2: A software for modeling functional spatio-temporal data | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 83 scored citations.