MF-SCM constructs synthetic control weights from mixed-frequency data, proves the estimator achieves the lowest possible squared prediction error among averaging methods, and derives asymptotic inference for the average treatment effect.
Asymptotic Properties of the Distributional Synthetic Controls
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abstract
As an alternative to synthetic control, the distributional Synthetic Control (DSC) proposed by Gunsilius (2023) provides estimates for quantile treatment effect and thus enabling researchers to comprehensively understand the impact of interventions in causal inference. But the asymptotic properties of DSC have not been built. In this paper, we first establish the DSC estimator's asymptotic optimality in the essence that the treatment effect estimator given by DSC achieves the lowest possible squared prediction error among all potential estimators from averaging quantiles of control units. We then establish the convergence rate of the DSC weights. A significant aspect of our research is that we find the DSC synthesis forms an optimal weighted average, particularly in situations where it is impractical to perfectly fit the treated unit's quantiles through the weighted average of the control units' quantiles. Simulation results verify our theoretical insights.
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stat.ME 1years
2026 1verdicts
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Synthetic Control Method with Mixed Frequency Data
MF-SCM constructs synthetic control weights from mixed-frequency data, proves the estimator achieves the lowest possible squared prediction error among averaging methods, and derives asymptotic inference for the average treatment effect.