Abstract
This paper develops an intertemporal propensity score matching (PSM)
approach for estimating the impact of covid-19 lockdowns on air pollution. While
PSM has been exclusively applied in the context of matching cross-sectional units,
this paper shows that, under specific circumstances, PSM can be also applied for
estimating causal inference by means of matching across different temporal units in
the context of multivariate time series data.We apply our intertemporal PSM model
to the data collected from a large number of air-pollution-measurement stations in
Northern Italy, estimating the casual effect of the March-May-2020 lockdown on
air-pollution without resorting to the more stringent functional form assumptions of
the existing literature
| Original language | English |
|---|---|
| Pages | 920-925 |
| Number of pages | 6 |
| Publication status | Published - 2022 |
| Event | 51st Scientific Meeting of the Italian Statistical Society - Caserta Duration: 1 Jan 2022 → … |
Conference
| Conference | 51st Scientific Meeting of the Italian Statistical Society |
|---|---|
| City | Caserta |
| Period | 1/01/22 → … |
Keywords
- propensity score matching
- air pollution
- coronavirus lockdown