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.
arXiv preprint arXiv:2203.06279 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
abstract
In this article we propose a set of simple principles to guide empirical practice in synthetic control studies. The proposed principles follow from formal properties of synthetic control estimators, and pertain to the nature, implications, and prevention of over-fitting biases within a synthetic control framework, to the interpretability of the results, and to the availability of validation exercises. We discuss and visually demonstrate the relevance of the proposed principles under a variety of data configurations.
fields
stat.ME 2years
2026 2representative citing papers
A Gamma–Bernoulli hierarchical prior lets Bayesian synthetic controls select donors and put exact zeros on excluded units while staying on the simplex, with posterior consistency and better recovery of sparse donors in simulations.
citing papers explorer
-
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.
-
Bayesian Donor Set Selection in Synthetic Controls
A Gamma–Bernoulli hierarchical prior lets Bayesian synthetic controls select donors and put exact zeros on excluded units while staying on the simplex, with posterior consistency and better recovery of sparse donors in simulations.