5/8/2023 0 Comments Xline stataAs stated before, this technique can be compared to difference-in-difference.Abadie and L’Hour (2020) also proposes a penalization method for performing the synthetic control method on disaggregated data.As a caveat to the previous bullet point, be wary of structural breaks when using large pre-intervention periods.Additionally, if aggregate data doesn’t exist, you can sometimes aggregate micro-level data to estimate aggregate values. Examples include state-level per-capita GDP, country-level crime rates, and state-level alcohol consumption statistics. Aggregate data is required for this method.Specifically, the pre-intervention time frame ought to be large enough to form an accurate estimate. Panel data is necessary for the synthetic control method and, typically, requires observations over many time periods.However, the donor pool must still share similar characteristics to the treatment unit in order to construct an accurate estimate. Unlike the difference-in-difference method, parallel trends aren’t a necessary assumption.Here is an excellent resource by Alberto Abadie (the economist who developed the method) if you’re interested in getting a more comprehensive overview of synthetic controls. The difference between the two curves, post-intervention, gives us our estimated treatment effect. The synthetic control follows a very similar path to the treated unit pre-intervention. Because our synthetic control was constructed from untreated units, when the intervention occurs at time \(T_0\), the difference between the synthetic control and the treated unit gives us our estimated treatment effect.Īs a last bit of intuition, below is a graph depicting the upshot of the method. , J 1\) units, assuming without loss of generality that the first unit is the treated unit, \(Y_\) (synthetic control) for all time periods \(t\). The idea is to construct a convex combination of similar untreated units (often referred to as the “donor pool”) to create a synthetic control that closely resembles the treatment subject and conduct counterfactual analysis with it. countries, states, counties) to estimate the effects of aggregate interventions. It is typically used with a small number of large units (e.g. Synthetic Control Method is a way of estimating the causal effect of an intervention in comparative case studies. This site uses Just the Docs, a documentation theme for Jekyll. Import a Delimited Data File (CSV, TSV).Graphing a By-Group or Over-Time Summary Statistic.Marginal Effects Plots for Interactions with Continuous Variables.Marginal effects plots for interactions with categorical variables.Line Graph with Labels at the Beginning or End of Lines.Marginal Effects in Nonlinear Regression.Density Discontinuity Tests for Regression Discontinuity.Random/Mixed Effects in Linear Regression.McFadden's Choice Model (Alternative-Specific Conditional Logit).Determine the Observation Level of a Data Set.Creating a Variable with Group Calculations.
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