Methods to detect and measures the impact of a policy instrument on a time series#
What are the methods used to detect the impact of a policy instrument on a time series? This post tracks my notes on the different existing methods : time-series modelisations (eg. ARIMA), synthetic controls
Papers : Stechemesser, A., Koch, N., Mark, E., Dilger, E., Klösel, P., Menicacci, L., … & Wenzel, A. (2024). Climate policies that achieved major emission reductions: Global evidence from two decades. Science, 385(6711), 884-892., Causal impact documentation, Abadie, A. (2021). Using synthetic controls: Feasibility, data requirements, and methodological aspects. Journal of economic literature, 59(2), 391-425., Tutorial in healthcare
Synthetic controls#
Theory#
Focus
Causal Impact#
Le paquet CausalImpact a été développé par google pour estimer l’effet d’intervention à partir de séries aggrégées.
Il s’appuie préférentiellement sur des méthodes bayésiennes mais est agnostique en terme d’estimateur de Machine Learning.
Detect significant break in the emissions and evaluate the impact of policy on the emission changes#
Here, I focus on the supplemental material of the paper.
They use a linear model called saturated two-way-fixed-effects predicting $log(CO2)_{i,t}$ from treatment and time-period indicators, control variables (gdp, population, heating degree days, cooling degree days and eu) and fixed country and period effects. They use a variable selection method to detect break. This is not the lasso, the method is called GETS-panel and is based on a block-search algorithm.